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In this paper, we study the existence and global exponential stability of almost automorphic solutions for Clifford-valued high-order Hopfield neural networks by direct method. That is to say, we do not decompose the systems under consideration into real-valued systems, but we directly study Clifford-valued systems. Our methods and results are new. Finally, an example is given to illustrate our main results. [ABSTRACT FROM AUTHOR]

Within the swiftly evolving domain of neural networks, the discrete Hopfield-SAT model, endowed with logical rules and the ability to achieve global minima of SAT problems, has emerged as a novel prototype for SAT solvers, capturing significant scientific interest. However, this model shows substantial sensitivity to network size and logical complexity. As the number of neurons and logical complexity increase, the solution space rapidly contracts, leading to a marked decline in the model's problem-solving performance. This paper introduces a novel discrete Hopfield-SAT model, enhanced by Crow search-guided fuzzy clustering hybrid optimization, effectively addressing this challenge and significantly boosting solving speed. The proposed model unveils a significant insight: its uniquely designed cost function for initial assignments introduces a quantification mechanism that measures the degree of inconsistency within its logical rules. Utilizing this for clustering, the model utilizes a Crow search-guided fuzzy clustering hybrid optimization to filter potential solutions from initial assignments, substantially narrowing the search space and enhancing retrieval efficiency. Experiments were conducted with both simulated and real datasets for 2SAT problems. The results indicate that the proposed model significantly surpasses traditional discrete Hopfield-SAT models and those enhanced by genetic-guided fuzzy clustering optimization across key performance metrics: Global minima ratio, Hamming distance, CPU time, retrieval rate of stable state, and retrieval rate of global minima, particularly showing statistically significant improvements in solving speed. These advantages play a pivotal role in advancing the discrete Hopfield-SAT model towards becoming an exemplary SAT solver. Additionally, the model features exceptional parallel computing capabilities and possesses the potential to integrate with other logical rules. In the future, this optimized model holds promise as an effective tool for solving more complex SAT problems. [ABSTRACT FROM AUTHOR]

This paper presents two new fault models for networked systems. These fault models are more realistic and generalized for networked systems in the sense that they can represent the effects of fault at the node and network levels. At the network layer, the uncertain effects of the network lines are modeled using a Markov chain with complex transition probabilities simultaneously with the stochastic behavior of the network using a Bernoulli process. A new output feedback-based controller, which is two-mode dependent and considers network uncertainties and output measurements for gain calculation, is presented. Using the tools of robust control and stochastic stability, linear matrix inequality-based sufficient conditions are derived. The proposed controller successfully maintains the system's performance by tolerating the effects of simultaneous sensor and actuator faults, ensuring the stability of networked loops. Simulation results verify the applicability of the presented fault-tolerant control against multiple simultaneous faults. [ABSTRACT FROM AUTHOR]

Value chain collaboration management is an effective means for enterprises to reduce costs and increase efficiency to enhance competitiveness. Vertical and horizontal collaboration have received much attention, but the current collaboration model combining the two is weak in terms of task assignment and node collaboration constraints in the whole production-distribution process. Therefore, in the enterprise dynamic alliance, this paper models the MVC (multi-value-chain) collaboration process for the optimization needs of the MVC collaboration network in production-distribution and other aspects. Then a MVC collaboration network optimization model is constructed with the lowest total production-distribution cost as the optimization objective and with the delivery cycle and task quantity as the constraints. For the high-dimensional characteristics of the decision space in the multi-task, multi-production end, multi-distribution end, and multi-level inventory production-distribution scenario, a genetic algorithm is used to solve the MVC collaboration network optimization model and solve the problem of difficult collaboration of MVC collaboration network nodes by adjusting the constraints among genes. In view of the multi-level characteristics of the production-distribution scenario, two chromosome coding methods are proposed: staged coding and integrated coding. Moreover, an algorithm ERGA (enhanced roulette genetic algorithm) is proposed with enhanced elite retention based on a SGA (simple genetic algorithm). The comparative experiment results of SGA, SEGA (strengthen elitist genetic algorithm), ERGA, and the analysis of the population evolution process show that ERGA is superior to SGA and SEGA in terms of time cost and optimization results through the reasonable combination of coding methods and selection operators. Furthermore, ERGA has higher generality and can be adapted to solve MVC collaboration network optimization models in different production-distribution environments. [ABSTRACT FROM AUTHOR]

• This paper studies the stabilization of nonlinear time-delay systems under flexible delayed impulsive control. • It is shown that the size of delay in continuous dynamics can be smaller or bigger than the impulsive intervals. • It removes the restriction on the magnitude relationship between the delay in continuous flow and the impulsive delay. • The rate coefficients are flexible and the impulsive delay can be integrated to guarantee the stabilization of impulses. • The synchronization of chaotic neural network is formalized in terms of linear matrix inequalities. This paper studies the stabilization of nonlinear time-delay systems under flexible delayed impulsive control. Some sufficient conditions are provided for establishing stability property in terms of exponential Lyapunov-Razumikhin functions. It is shown that the size of delay in continuous dynamics can be flexible. Specially, it can be smaller or larger than the impulsive intervals, and there is no magnitude relationship between the delay in continuous flow and impulsive delay. In most existing results, from the impulsive control point of view, the Lyapunov functions were based on the assumption that there was a common threshold at every impulse point. In this study, utilizing the proposed method of average impulsive estimation (AIE), the rate coefficients are flexible, and the impulsive delay can be integrated to guarantee the effect of stabilization of impulses. As an application, the theoretical results are applied to the synchronization of a chaotic neural network, and the impulsive control input is formalized in terms of linear matrix inequalities (LMIs). The efficiency of the derived results is illustrated by two numerical examples. [ABSTRACT FROM AUTHOR]

• This paper studies the leader-following flocking problem of the unmanned aerial vehicle (UAV). • It employs hybrid event-triggered control and pinning mechanism with delayed control inputs. • Criteria on flocking stability and Zeno behaviour prevention are derived. • It combines both displacement-based control and gyroscopic forces. • The tolerance of network instability in terms of continuous part is increased. This paper studies the leader-following flocking control problem of unmanned aerial vehicle (UAV) systems by integrating a hybrid impulsive framework, where the full exchange of information only occurs at each impulsive time instant. It is also shown that the overall stability can be achieved via impulses while the continuous dynamics remains destabilizing. Meanwhile, the even-triggered and pinning mechanisms are considered to further reduce control resource usage and transmission redundancy. Moreover, topology switching and strong nonlinearity are taken into account for more practical application. Based on transmission topology structure, impulsive control theory, and Lyapunov based event-triggered strategy, some improved theorems are derived to guarantee convergence of the corresponding error dynamics without exhibiting Zeno behaviour. Compared with most existing results on continuous or impulsive systems, the obtained stability criteria are more general as they are applicable to systems involving both delayed coupling feedback and delayed impulses. In addition, a novel approach using braking and gyroscopic forces is utilized for collision avoidance purposes. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

We introduce a non-instantaneous impulsive Hopfield neural network model in this paper. Firstly, we prove the existence and uniqueness of an almost periodic solution of this model. Secondly, we prove that the solution of this model is exponentially stable. Finally, we give an example of this model. [ABSTRACT FROM AUTHOR]

The study first proposes the difficult nonlinear convergent radius and convergent rate formulas and the complete derivations of a mathematical model for the nonlinear five-link human biped robot (FLHBR) system which has been a challenge for engineers in recent decades. The proposed theorem simultaneously has very distinctive superior advantages including the stringent almost disturbance decoupling feature that addresses the major deficiencies of the traditional singular perturbation approach without annoying "complete" conditions for the discriminant function and the global exponential stability feature without solving the impractical Hamilton–Jacobi equation for the traditional H-infinity technique. This article applies the feedback linearization technique to globally stabilize the FLHBR system that greatly improved those shortcomings of nonlinear function approximator and make the effective working range be global for whole state space, whereas the traditional Jacobian linearization technique is valid only for areas near the equilibrium point. In order to make some comparisons with traditional approaches, first example of the representative ones, that cannot be addressed well for the pioneer paper, is shown to demonstrate the fact that the effectiveness of the proposed main theorem is better than the traditional singular perturbation technique. Finally, we execute a second simulation example to compare the proposed approach with the traditional PID approach. The simulation results show that the transient behaviors of the proposed approach including the peak time, the rise time, the settling time and the maximum overshoot specifications are better than the traditional PID approach. [ABSTRACT FROM AUTHOR]

Real life logical rule is not always satisfiable in nature due to the redundant variable that represents the logical formulation. Thus, the intelligence system must be optimally governed to ensure the system can behave according to non-satisfiable structure that finds practical applications particularly in knowledge discovery tasks. In this paper, we a propose non-satisfiability logical rule that combines two sub-logical rules, namely Maximum 2 Satisfiability and Random 2 Satisfiability, that play a vital role in creating explainable artificial intelligence. Interestingly, the combination will result in the negative logical outcome where the cost function of the proposed logic is always more than zero. The proposed logical rule is implemented into Discrete Hopfield Neural Network by computing the cost function associated with each variable in Random 2 Satisfiability. Since the proposed logical rule is difficult to be optimized during training phase of DHNN, Election Algorithm is implemented to find consistent interpretation that minimizes the cost function of the proposed logical rule. Election Algorithm has become the most popular optimization metaheuristic technique for resolving constraint optimization problems. The fundamental concepts of Election Algorithm are taken from socio-political phenomena which use new and efficient processes to produce the best outcome. The behavior of Random Maximum 2 Satisfiability in Discrete Hopfield Neural Network is investigated based on several performance metrics. The performance is compared between existing conventional methods with Genetic Algorithm and Election Algorithm. The results demonstrate that the proposed Random Maximum 2 Satisfiability can become the symbolic instruction in Discrete Hopfield Neural Network where Election Algorithm has performed as an effective training process of Discrete Hopfield Neural Network compared to Genetic Algorithm and Exhaustive Search. [ABSTRACT FROM AUTHOR]

In this digital age, images have become one of the most important digital information. In order to ensure the security of images, various image encryption algorithms have come out one after another. Many excellent characteristics of chaotic maps can effectively enhance the stability of image encryption algorithms. Image encryption algorithms based on chaotic systems have become the focus of image encryption algorithm research. This paper proposes an image encryption algorithm combining neural network and chaotic map. A chaos matrix is generated by the Hopfield neural network model for image diffusion. The keys can be selected in the range of pixel values, and the keys space is larger. In this paper, the value of the keys are selected as three random pixel values of the image after the separation of the three primary colors of the plain. Because the pixel value of each image is different, the key is also different. Each image generates a different chaotic sequence, achieving "one key at a time". A chaos matrix is generated by Lorenz chaotic system for image scrambling. The diffusion and scrambling are carried out at the same time. Several indicators are analyzed in the experiment, and the experimental results show that the algorithm improves the key sensitivity and plain sensitivity, expands the key space, and can resist some common attacks, such as differential attack, individual diffusion attack, individual scrambling attack, etc. [ABSTRACT FROM AUTHOR]

The computation of the Moore–Penrose inverse is widely encountered in science and engineering. Due to the parallel‐processing nature and strong‐learning ability, the neural network has become a promising approach to solving the Moore–Penrose inverse recently. However, almost all the existing neural networks for matrix inversion are based on the gradient‐descent (GD) method, whose main drawbacks are slow convergence and sensitivity to learning parameters. Moreover, there is no unified neural network to compute the Moore–Penrose inverse for both the full‐rank matrix and rank‐deficient matrix. In this paper, an efficient second‐order neural network model with the improved Newton's method is proposed to obtain the accurate Moore–Penrose inverse of an arbitrary matrix by one epoch without any learning parameter. Compared with the GD‐based neural networks for Moore–Penrose inverse computation, the proposed model converges faster and has lower complexity. Furthermore, through in‐depth derivation, the neural network for computing the Moore–Penrose inverse is well interpretable. Numerical studies and application to the random matrix inversion in multiple‐input multiple‐output detection are provided to validate the efficiency of the proposed model for solving the Moore–Penrose inverse. [ABSTRACT FROM AUTHOR]

This paper shows our new contributions on data driven model predictive control, such as persistent excitation, optimal state feedback controller, output predictor and stability. After reviewing the definition of persistent excitation and its important property, the idea of data driven is introduced in model predictive control to construct our considered data driven model predictive control, whose state information and output variable are generated by measured data online. Variation tool is applied to obtain the optimal controller or predictive controller through our own derivation. Furthermore, for the cost function in data driven model predictive control, its preliminary stability is analysed by using the linear matrix inequality and one single optimal state feedback controller is given. To bridge the gap between our derived results and other control strategies, output predictor is constructed from the point of data driven idea, i.e. using some collected input–output data from one experiment to establish the output predictor at any later time instant. Finally, one simulation example is given to prove the efficiency of our derived results. [ABSTRACT FROM AUTHOR]

There is no doubt that the accurate assessment of local magnetization in three-dimensional objects exhibiting hysteresis is crucial to accurate performance estimation of a variety of electromagnetic devices. Recently, it has been demonstrated that a Stoner-Wohlfarth-like elementary hysteresis operator may be constructed using two-node Hopfield neural network (HNN) having internal positive feedback. Based upon the previously mentioned approach, this paper presents a methodology using which local magnetization in 3D objects exhibiting hysteresis may be assessed. The approach utilizes a four-node tetrahedron-shaped HNN with activation functions constructed using a weighted superposition of a step and sigmoidal functions in accordance with the M − H curve of the material under consideration. In this approach, the internal feedback factors between the different nodes for any tetrahedron are dependent on its geometrical configuration. Hence, shape configuration effects on the magnetization patterns of any three-dimensional object approximated by an ensemble of tetrahedra are implicitly taken into consideration. To demonstrate the applicability of the proposed approach, computations were carried out for different three-dimensional magnetized bodies having different M − H curves. Theoretical and computational details of the approach are given in the paper. [ABSTRACT FROM AUTHOR]

This paper investigates the stochastic stability and stabilization of positive Markov jump linear impulsive systems (PMJLIS) with time delay. First, by choosing appropriate co‐positive Lyapunov–Krasovskii functional and using the impulsive average dwell‐time method, a stochastic stability criterion is provided for PMJLIS with time delay. Based on this criterion, for PMJLIS with time delay, the stabilization conditions are given using state feedback and impulse feedback, respectively. According to this criteria, the stochastic stability of the systems can be judged and the controllers can be designed only from some relations of the coefficient matrices, Markov jump parameters and delay. To illustrate the main results, a simulation example is provided at the end with a state‐feedback controller and an impulsive controller is designed, respectively, which makes the corresponding closed‐loop systems be positive, globally asymptotically stable in probability and globally asymptotically stable in mean. [ABSTRACT FROM AUTHOR]

Dynamic complex matrix equation (DCME) is frequently encountered in the fields of mathematics and industry, and numerous recurrent neural network (RNN) models have been reported to effectively find the solution of DCME in no noise environment. However, noises are unavoidable in reality, and dynamic systems must be affected by noises. Thus, the invention of antinoise neural network models becomes increasingly important to address this issue. By introducing a new activation function (NAF), a robust zeroing neural network (RZNN) model for solving DCME in noisy-polluted environment is proposed and investigated in this paper. The robustness and convergence of the proposed RZNN model are proved by strict mathematical proof and verified by comparative numerical simulation results. Furthermore, the proposed RZNN model is applied to manipulator trajectory tracking control, and it completes the trajectory tracking task successfully, which further validates its practical applied prospects. [ABSTRACT FROM AUTHOR]

Infected individuals often obtain or lose immunity after recovery in medical studies. To solve the problem, this paper proposes a stochastic SIRS epidemic model with a general incidence rate and partial immunity. Through an appropriate Lyapunov function, we obtain the existence and uniqueness of a unique globally positive solution. The disease will be extinct under the threshold criterion. We analyze the asymptotic behavior around the disease-free equilibrium of a deterministic SIRS model. By using the Khasminskii method, we prove the existence of a unique stationary distribution. Further, solutions of the stochastic model fluctuate around endemic equilibrium under certain conditions. Some numerical examples illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper proposes a new angular velocity observer for attitude tracking of spacecraft based on contraction analysis. The observer is designed in the inertial reference frame via estimating the inertial angular momentum to avoid the square term of the angular velocity when the spacecraft dynamics is expressed in the body frame. It employs a continuous angular-velocity dependent innovation term generated by means of a simple first-order linear filter, instead of a discontinuous attitude-dependent innovation term commonly used in angular velocity observer designs, resulting in a smooth behavior. The global exponential convergence is achieved. Moreover, when combined with the exponentially convergent tracking controller devised in this paper, it gives an overall system with exponential stability relying on a separation property. Finally, a switching function with hysteresis is introduced to stabilize the closest equilibrium in the configuration space, achieving the global exponential stability. Numerical simulations are included to illustrate the performance of the proposed observer in the closed loop, comparison with similar results, and robustness verification under inertia parameter uncertainties and noisy measurements. • A novel angular velocity observer with separation property is designed. • Global exponential convergence is shown for the proposed observer, relaying on contraction analysis. • Two attitude tracking controllers are developed in a controller-observer scheme, resulting in an overall convergent system in view of the contraction of hierarchical systems. • The separation property and robustness of the proposed controller-observer schemes are analyzed and illustrated through numerical simulations. [ABSTRACT FROM AUTHOR]

The complex dynamics analysis of fractional-order neural networks is a cutting-edge topic in the field of neural network research. In this paper, a fractional-order Hopfield neural network (FOHNN) system is proposed, which contains four neurons. Using the Adomian decomposition method, the FOHNN system is solved. The dissipative characteristics of the system are discussed, as well as the equilibrium point is resolved. The characteristics of the dynamics through the phase diagram, the bifurcation diagram, the Lyapunov exponential spectrum, and the Lyapunov dimension of the system are investigated. The circuit of the system was also designed, based on the Multisim simulation platform, and the simulation of the circuit was realized. The simulation results show that the proposed FOHNN system exhibits many interesting phenomena, which provides more basis for the study of complex brain working patterns, and more references for the design, as well as the hardware implementation of the realized fractional-order neural network circuit. [ABSTRACT FROM AUTHOR]

This paper presents a new scheme for the online solution of a networked multi-agent pursuit–evasion game based on an online adaptive dynamic programming method. As a multi-agent in the game can form an Internet of Things (IoT) system, by incorporating the relative distance and the control energy as the performance index, the expression of the policies when the agents reach the Nash equilibrium is obtained and proved by the minmax principle. By constructing a Lyapunov function, the capture conditions of the game are obtained and discussed. In order to enable each agent to obtain the policy for reaching the Nash equilibrium in real time, the online adaptive dynamic programming method is used to solve the game problem. Furthermore, the parameters of the neural network are fitted by value function approximation, which avoids the difficulties of solving the Hamilton-Jacobi–Isaacs equation, and the numerical solution of the Nash equilibrium is obtained. Simulation results depict the feasibility of the proposed method for use on multi-agent pursuit–evasion games. [ABSTRACT FROM AUTHOR]

In this paper, the periodicity of a class of nonautonomous fuzzy neural networks with impulses, reaction-diffusion terms, and distributed time delays are investigated. By establishing an integro-differential inequality with impulsive initial conditions and time-varying coefficients, employing the M -matrix theory, Poincar mappings, and fixed point theory, several new sufficient conditions to ensure the periodicity and global exponential stability of the formulated system are obtained. It is worthwhile to mention that our technical methods are practical, in the sense that all new stability conditions are stated in simple algebraic forms, and an optimization method is provided to estimate the exponential convergence rate, so their verification and applications are straightforward and convenient. The validity and generality of our methods are illustrated by two numerical examples. [ABSTRACT FROM AUTHOR]

Microgrids possess rotating generators and inverter‐based renewable resources and have limited inertia. The stability of a microgrid is affected by the parameters of controller and system equipment. This paper analyzes the set of all admissible parameters that guarantee the small‐signal stability of a multi‐source microgrid, which is called the small‐signal stability region (SSSR). First, the stability impact of parameters varying in a given hypercube is studied. A sufficient robust stability condition is proposed with a hypercube parameter set, giving rise to linear matrix inequalities. The maximal hypercube ensuring stability can be found via dichotomy. Then, the SSSR is constructed via the union of hypercubes centered at different points. A special partitioning‐and‐pruning algorithm is proposed to search the parameter space and reduce the number of hypercube regions. Case studies are conducted on a testing system with a micro‐turbine generator and two inverter‐based generators. The SSSR is visualized, illustrating the interaction among generators and the stability impact of loads. The proposed method provides a useful reference for system design and security assessment. [ABSTRACT FROM AUTHOR]

The issue of H ∞ /passive master–slave synchronization for Markov jumping neural networks with reaction–diffusion terms is investigated in this paper via an event-triggered control scheme under deception attacks. To lighten the burden of limited communication bandwidth as well as ensure the control performance, an event-triggered transmission scheme is developed. Meanwhile, the randomly occurring deception attacks, which received from the event generator are assumed to modify the sign of the control signal, are taken into account. Furthermore, sufficient conditions ensuring the prescribed H ∞ /passive performance level of the neural networks, are deduced beyond Lyapunov stability theory, and the controller gains are derived dealing with the matrix convex optimization problem. At last, the availability of the approach proposed is demonstrated via a numerical example. • Different from references (Dharani et al., 2017; Wang et al., 2021), this paper considers RDTs and parameter uncertainty. • The random deception attacks is considered. ETTS is used to save bandwidth resources. • By using LKF and inequalities, the H ∞ /passive synchronization performance is achieved. [ABSTRACT FROM AUTHOR]

Synaptic crosstalk is an important biological phenomenon that widely exists in neural networks. The crosstalk can influence the ability of neurons to control the synaptic weights, thereby causing rich dynamics of neural networks. Based on the crosstalk between synapses, this paper presents a novel two-neuron based memristive Hopfield neural network with a hyperbolic memristor emulating synaptic crosstalk. The dynamics of the neural networks with varying memristive parameters and crosstalk weights are analyzed via the phase portraits, time-domain waveforms, bifurcation diagrams, and basin of attraction. Complex phenomena, especially coexisting dynamics, chaos and transient chaos emerge in the neural network. Finally, the circuit simulation results verify the effectiveness of theoretical analyses and mathematical simulation and further illustrate the feasibility of the two-neuron based memristive Hopfield neural network hardware. [ABSTRACT FROM AUTHOR]

Since entering the new century, People’s living standards are constantly improving, with the continuous improvement of living conditions, people are becoming more and more important in education, which is the embodiment of the enhancement of national strength. The education level is getting higher and higher, and a good education level needs a good teacher–student relationship. To solve these problems, we use the emotional cognition of God’s network to study the teacher–student relationship, and collect and analyze the data of the teacher–student relationship. In this chapter, we use GABP neural network algorithm DHNN algorithm and discrete Hopfield neural network to make the collected data more convenient to be analyzed. The research shows that there is a close relationship between the educational level and the relationship between teachers and students in China, and a good relationship between teachers and students will promote the improvement of educational level. According to the research data, “face-to-face” is the most important way of interaction between tutors and postgraduates in various types of teacher– student relationship. QQ WeChat is also one of the main ways of interaction between students and teachers, which shows that the interaction between students and teachers is talking about the interaction between online and Internet. The education industry is becoming more and more important, and the teacher–student relationship is the most important part of the education industry. Good teacher–student relationship is helpful to cultivate students’ healthy personality. In view of the cold relationship between teachers and students at present, we need to make some measures the relationship between teachers and students and effectively use the relationship between teachers and students to promote the better development of the education industry. [ABSTRACT FROM AUTHOR]

The topic of this paper is to study the numerical solution of the fourth-order partial differential equation and analyze its visual application in software simulation. Therefore, for the initial circular domain, the expansion should be transformed into a fourth-order problem in the plane dimension. Then, we introduced appropriate-weighted Sobolev space based on the improved neural network algorithm and established a weak form and the corresponding discrete form for each one-dimensional fourth-order problem. The approximation properties of the cubic Hermite interpolation operator are used to verify the error value of the approximation solution. After obtaining the relevant algorithms, numerical empirical analysis is carried out to prove that the proposed algorithm is effective. Therefore, this article applies it to the visualization simulation technology, and the visualization module mainly completes two tasks: collecting geometric data and drawing models. At present, the application of the visualization module in the program mainly has two aspects: on the one hand, the boundary information of geometry can be obtained by screening the existing database so that the boundary model can be displayed in the visualization; on the other hand, from the calculation result file. Read the geometric information of particles and boundaries and perform dynamic simulation playback of the calculated results. [ABSTRACT FROM AUTHOR]

The passivity, low power consumption, memory characteristics and nanometer size of memristors make them the best choice to simulate synapses in artificial neural networks. In this paper, based on the proposed associative memory rules, we design a memristor neural network with plasticity synapses, which can perform analog operations similar to its biological behavior. For the memristor neural network circuit, we also construct a relatively simple Pavlov's dog experiment simulation circuit, which can effectively reduce the complexity and power consumption of the network. Some advanced neural activities including learning, associative memory and three kinds of forgetting are realized based on the spiking-rate-dependent plasticity rule. Finally, the Simulation program with integrated circuit emphasis is used to simulate the circuit. The simulation results not only prove the correctness of the design, but also help to realize more efficient, simpler and more complex analog circuit of memristor neural network and then help to realize more intelligent, smaller and low-power brain chips. [ABSTRACT FROM AUTHOR]

Currently, most real-world time series datasets are multivariate and are rich in dynamical information of the underlying system. Such datasets are attracting much attention; therefore, the need for accurate modelling of such high-dimensional datasets is increasing. Recently, the deep architecture of the recurrent neural network (RNN) and its variant long short-term memory (LSTM) have been proven to be more accurate than traditional statistical methods in modelling time series data. Despite the reported advantages of the deep LSTM model, its performance in modelling multivariate time series (MTS) data has not been satisfactory, particularly when attempting to process highly non-linear and long-interval MTS datasets. The reason is that the supervised learning approach initializes the neurons randomly in such recurrent networks, disabling the neurons that ultimately must properly learn the latent features of the correlated variables included in the MTS dataset. In this paper, we propose a pre-trained LSTM-based stacked autoencoder (LSTM-SAE) approach in an unsupervised learning fashion to replace the random weight initialization strategy adopted in deep LSTM recurrent networks. For evaluation purposes, two different case studies that include real-world datasets are investigated, where the performance of the proposed approach compares favourably with the deep LSTM approach. In addition, the proposed approach outperforms several reference models investigating the same case studies. Overall, the experimental results clearly show that the unsupervised pre-training approach improves the performance of deep LSTM and leads to better and faster convergence than other models. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the scale of gas concentration data, in order to meet the requirements of mass data processing, this paper used the strong advantages of cloud computing in the processing of large data sets to build the framework of coal mine gas concentration under the cloud platform, proposed a genetic optimization Elma neural network model based on cloud computing, and carried out experiments based on the massive data of a coal mine in Tangshan. It has been proved that its mean square error is basically stable within 0.05, reaching the acceptable error range. This algorithm is both efficient and feasible in short-term prediction of coal mine gas concentration. [ABSTRACT FROM AUTHOR]

In this paper, a new class of bidimensional fractional linear systems is considered. The stability radius of the disturbed system is described according to the H8 norm. Sufficient conditions to ensure the stability margins of the closed-loop system are offered in terms of linear matrix inequalities. The concept of D stability region for these systems is also considered. Examples are provided to verify the applicability of our main result. [ABSTRACT FROM AUTHOR]

This paper intensively studied the finite-time (FNT) and fixed-time (FXT) synchronization issues for complex networks (CNs) with semi-Markovian switching and impulsive effect. The impulses are assumed to be independent of the semi-Markovian switching. Firstly, a unified FNT and FXT stability criterion of impulsive dynamical system with time-varying delays is extended by comparison principle. Secondly, two novel hybrid control schemes, which are composed of adaptive gain and switching state-feedback are proposed. Thirdly, by employing Kronecker product, Lyapunov-Krasovskii functional and inequality technique, FNT and FXT synchronization criteria for impulsive CNs with semi-Markovian switching are presented in a set of low-dimensional linear matrix inequalities, and the settling times are computed respectively. Finally, simulations are given to verify the proposed adaptive FNT and FXT synchronization criteria. • A unified finite/fixed-time stability criterion of impulsive system is extended. • The impulsive effect and semi-Markovian switching are considered in complex networks. • Under two hybrid controllers, the finite/fixed-time synchronization criteria are established. • The finite/fixed-time synchronization settling times are given by some concrete expressions. [ABSTRACT FROM AUTHOR]

The deterioration of infrastructure's health has become more predominant on a global scale during the 21st century. Aging infrastructure as well as those structures damaged by natural disasters have prompted the research community to improve state-of-the-art methodologies for conducting Structural Health Monitoring (SHM). The necessity for efficient SHM arises from the hazards damaged infrastructure imposes, often resulting in structural collapse, leading to economic loss and human fatalities. Furthermore, day-to-day operations in these affected areas are limited until an inspection is performed to assess the level of damage experienced by the structure and the required rehabilitation determined. However, human-based inspections are often labor-intensive, inefficient, subjective, and restricted to accessible site locations, which ultimately negatively impact our ability to collect large amounts of data from inspection sites. Though Deep-Learning (DL) methods have been heavily explored in the past decade to rectify the limitations of traditional methods and automate structural inspection, data scarcity continues to remain prevalent within the field of SHM. The absence of sufficiently large, balanced, and generalized databases to train DL-based models often results in inaccurate and biased damage predictions. Recently, Generative Adversarial Networks (GANs) have received attention from the SHM community as a data augmentation tool by which a training dataset can be expanded to improve the damage classification. However, there are no existing studies within the SHM field which investigate the performance of DL-based multiclass damage identification using synthetic data generated from GANs. Therefore, this paper investigates the performance of a convolutional neural network architecture using synthetic images generated from a GAN for multiclass damage detection of concrete surfaces. Through this study, it was determined the average classification performance of the proposed CNN on hybrid datasets decreased by 10.6% and 7.4% for validation and testing datasets when compared to the same model trained entirely on real samples. Moreover, each model's performance decreased on average by 1.6% when comparing a singular model trained with real samples and the same model trained with both real and synthetic samples for a given training configuration. The correlation between classification accuracy and the amount and diversity of synthetic data used for data augmentation is quantified and the effect of using limited data to train existing GAN architectures is investigated. It was observed that the diversity of the samples decreases and correlation increases with the increase in the number of synthetic samples. [ABSTRACT FROM AUTHOR]

Mass sports has become a world trend, setting off a new health revolution in the world. Mass fitness programs not only enrich people's lives. It not only relieves the psychological pressure of modern people but also promotes people's health and improves people's quality of life. According to the time-consuming stability of neural network algorithm, this paper proposes a sports video recognition algorithm based on BP neural network. The static and dynamic features are classified by BP neural network, and the basic probability assignment is constructed according to the preliminary recognition results. At the same time, we use evidence theory to fuse the preliminary results and get the results of motion video recognition. It can be applied to the generation model of the feasible scheme of mass sports fitness. Relevant experiments show that the whole model that generates the feasible mass sports fitness scheme can accurately generate the sports fitness scheme of multiple patient users and ensure the rationality and safety of the sports fitness scheme. [ABSTRACT FROM AUTHOR]

This paper presents a novel random vector functional link (RVFL) formulation called the 1-norm RVFL (1N RVFL) networks, for solving the binary classification problems. The solution to the optimization problem of 1N RVFL is obtained by solving its exterior dual penalty problem using a Newton technique. The 1-norm makes the model robust and delivers sparse outputs, which is the fundamental advantage of this model. The sparse output indicates that most of the elements in the output matrix are zero; hence, the decision function can be achieved by incorporating lesser hidden nodes compared to the conventional RVFL model. 1N RVFL produces a classifier that is based on a smaller number of input features. To put it another way, this method will suppress the neurons in the hidden layer. Statistical analyses have been carried out on several real-world benchmark datasets. The proposed 1N RVFL with two activation functions viz., ReLU and sine are used in this work. The classification accuracies of 1N RVFL are compared with the extreme learning machine (ELM), kernel ridge regression (KRR), RVFL, kernel RVFL (K-RVFL) and generalized Lagrangian twin RVFL (GLTRVFL) networks. The experimental results with comparable or better accuracy indicate the effectiveness and usability of 1N RVFL for solving binary classification problems. [ABSTRACT FROM AUTHOR]

The subject of this paper is a programmable con trol system for a robotic manipulator. Considering the complex nonlinear dynamics involved in practical applications of systems and robotic arms, the traditional control method is here replaced by the designed Elma and adaptive radial basis function neural network—thereby improving the system stability and response rate. Related controllers and compensators were developed and trained using MATLAB-related software. The training results of the two neural network controllers for the robot programming trajectories are presented and the dynamic errors of the different types of neural network controllers and two control methods are analyzed. [ABSTRACT FROM AUTHOR]

Fractional-order chaotic systems are widely used in the field of encryption because of its initial value sensitivity and historical memory. In this paper, the fractional-order definition of Caputo is introduced based on a nonideal flux-controlled memristive Hopfield neural network model, when changing the parameters of the fractional-order memristive Hopfield neural network (FMHNN) can generate a different amount of multi-scroll attractors. Some dynamical behaviors are investigated by numerical simulation, especially analyzed coexistence and bifurcation under different orders and different coupling strengths. The results show that the chaotic system of FMHNN has abundant dynamic behaviors. In addition, a chaotic audio encryption scheme under a Message Queueing Telemetry Transport (MQTT) protocol is proposed and implemented by Raspberry Pi; the audio encryption system based on FMHNN has a broad future in intelligent home and other IoT applications. [ABSTRACT FROM AUTHOR]

In this paper, a hyperchaotic four-dimensional fractional discrete Hopfield neural network system (4D-FDHNN) with four positive Lyapunov exponents is proposed. Firstly, the chaotic dynamics' characteristics of the system are verified by analyzing and comparing the iterative trajectory diagram, phase diagram, attractor diagram, 0-1 test, sample entropy, and Lyapunov exponent. Furthermore, a novel image encryption scheme is designed to use the chaotic system as a pseudo-random number generator. In the scenario, the confusion phase using the fractal idea proposes a fractal-like model scrambling method, effectively enhancing the complexity and security of the confusion. For the advanced diffusion phase, we proposed a kind of Hilbert dynamic random diffusion method, synchronously changing the size and location of the pixel values, which improves the efficiency of the encryption algorithm. Finally, simulation results and security analysis experiments show that the proposed encryption algorithm has good efficiency and high security, and can resist common types of attacks. [ABSTRACT FROM AUTHOR]

In this paper, a delayed-within-host-dengue infection model with mitosis and immune response is analyzed. The basic reproduction number is calculated and a detailed discussion on the local and global dynamics of the model is conducted. By using comparison arguments, it is shown that when the basic reproduction number is less than unity, the infection-free equilibrium is globally asymptotically stable. When the basic reproduction number is greater than unity, the existence of Hopf bifurcation and stability switch at the immunity-activated infection equilibrium of the model with or without delay is established. Furthermore, by means of Lyapunov functional and LaSalle's invariance principle, sufficient conditions are obtained for the global stability of the immunity-activated infection equilibrium. Numerical simulations are given to illustrate the main theoretical results. The normal form is calculated to analyze some properties of the bifurcation periodic solution when the time delay is absent. Moreover, we carry out sensitivity analysis on basic reproduction number to determine crucial parameters that affect the stability of each of feasible equilibrium. [ABSTRACT FROM AUTHOR]

To reduce the delay caused by the interweaving and parallel driving of multiple left-turn vehicles and through vehicles upstream of the intersection entrance, the influencing factors and determination methods of unconventional left-turn lanes are studied in right-hand traffic (RHT) countries. For countries driving right, left-turn lanes are usually on the inside of roads. However, when there are a large number of vehicles turning left in the outer lane of the upstream section of the intersection, these vehicles will be forced to pass many consecutive parallel lanes and then enter the left-turn lane. During this process, many traffic conflicts will occur between left-turning vehicles and going-straight vehicles, which will lead to longer traffic delays. To reduce traffic conflicts and delays caused by problems mentioned before, a scheme of setting left-turn lanes abroad is proposed, and major influencing factors and judgment methods of such a scheme are also studied. With the help of traffic simulation software VISSIM, the simulation model of intersection entrance with a different number of through lanes, length of weaving section and left turn inner and outer lanes is established. By inputting different numbers of entry through vehicles and left-turning vehicles in the outer lane, the delay data under different geometric and traffic conditions are obtained for simulation analysis. With the help of MATLAB software, this paper analyzes the influence of the length of the weaving area and the number of left-turning vehicles on the delay of inside and outside left-turning lanes under the condition of a different number of straight vehicles, as well as the variation law between them. By inputting parameters such as the length of the weaving area and the number of lanes, go-straight vehicles and left-turning vehicles into the system of VISSIM, a BP neural network model is constructed and trained. When investigating the entrances of four intersections, the BP neural network model is used to analyze and calculate the traffic delay and determine the setting scheme of the inside or outside of the left-turn lane. Through experiments and further studies, a phenomenon was found: When more vehicles chose to turn left or go straight in the outside lane, the length of the weaving area will become shorter, and the delay reduction effect of the unconventional left-turn lane will more obvious. The specific location of the left-turn lane should be determined by the constructed BP neural network model through the comparative analysis of delay, and the judgment results are in good agreement with the realistic scheme. [ABSTRACT FROM AUTHOR]

The 21st century is an era of rapid development of information and frequent international exchanges, and Japanese language teaching has received increasing attention. Because of this, colleges and universities are now focused on improving the quality of Japanese education, both now and in the future. We need to boost the whole management of teaching quality, notably the assessment of instructors' teaching quality, in order to improve teaching quality. However, because a number of factors influence the quality of instruction, and each factor's weight varies, the evaluation results are difficult to express in a mathematical analytical formula, resulting in a complex nonlinear classification problem that traditional classification methods cannot solve well. As a new technology, as a result of the artificial neural networks (ANNs) fundamental qualities, it has been extensively applied in different evaluation issues for pattern recognition, nonlinear classification, and other research. This subject introduces the optimized deep neural network theory into Japanese teaching quality evaluation and completes the following work: (1) the algorithm of discrete Hopfield neural network is introduced in detail, and the neural network theory is introduced into teaching evaluation. (2) Then, based on the evaluation data of teachers' teaching quality in a school, a large number of simulation experiments and training were carried out, and a neural network model for evaluation of teachers' teaching effect was constructed and designed. Experiments reveal that the neural network model proposed in this paper is a nonlinear mapping method, which increases the evaluation's dependability and makes the outcomes more effective and objective. [ABSTRACT FROM AUTHOR]

Hybridized algorithms are commonly employed to improve the performance of any existing method. However, an optimal learning algorithm composed of evolutionary and swarm intelligence can radically improve the quality of the final neuron states and has not received creative attention yet. Considering this issue, this paper presents a novel metaheuristics algorithm combined with several objectives—introduced as the Hybrid Election Algorithm (HEA)—with great results in solving optimization and combinatorial problems over a binary search space. The core and underpinning ideas of this proposed HEA are inspired by socio-political phenomena, consisting of creative and powerful mechanisms to achieve the optimal result. A non-systematic logical structure can find a better phenomenon in the study of logic programming. In this regard, a non-systematic structure known as Random k Satisfiability (RANkSAT) with higher-order is hosted here to overcome the interpretability and dissimilarity compared to a systematic, logical structure in a Discrete Hopfield Neural Network (DHNN). The novelty of this study is to introduce a new multi-objective Hybrid Election Algorithm that achieves the highest fitness value and can boost the storage capacity of DHNN along with a diversified logical structure embedded with RANkSAT representation. To attain such goals, the proposed algorithm tested four different types of algorithms, such as evolutionary types (Genetic Algorithm (GA)), swarm intelligence types (Artificial Bee Colony algorithm), population-based (traditional Election Algorithm (EA)) and the Exhaustive Search (ES) model. To check the performance of the proposed HEA model, several performance metrics, such as training–testing, energy, similarity analysis and statistical analysis, such as the Friedman test with convergence analysis, have been examined and analyzed. Based on the experimental and statistical results, the proposed HEA model outperformed all the mentioned four models in this research. [ABSTRACT FROM AUTHOR]

One of the main problems in representing information in the form of nonsystematic logic is the lack of flexibility, which leads to potential overfitting. Although nonsystematic logic improves the representation of the conventional k Satisfiability, the formulations of the first, second, and third-order logical structures are very predictable. This paper proposed a novel higher-order logical structure, named G-Type Random k Satisfiability, by capitalizing the new random feature of the first, second, and third-order clauses. The proposed logic was implemented into the Discrete Hopfield Neural Network as a symbolic logical rule. The proposed logic in Discrete Hopfield Neural Networks was evaluated using different parameter settings, such as different orders of clauses, different proportions between positive and negative literals, relaxation, and differing numbers of learning trials. Each evaluation utilized various performance metrics, such as learning error, testing error, weight error, energy analysis, and similarity analysis. In addition, the flexibility of the proposed logic was compared with current state-of-the-art logic rules. Based on the simulation, the proposed logic was reported to be more flexible, and produced higher solution diversity. [ABSTRACT FROM AUTHOR]

Machine learning-based methods have emerged as competitors to traditional metaheuristic-based solutions in many areas. Besides investigating their effectiveness, it raises the question of whether these methods can be combined. This paper presents a systematic literature review based on P.R.I.S.M.A. methodology to provide a state-of-the-art overview of machine learning-assisted metaheuristics, focusing on local search algorithms such as Hill Climbing, Tabu Search, and Simulated Annealing. The review is based on a comprehensive evaluation of 48 related articles. These studies illustrate the most common applications of hybrid methods in various fields, including physical simulations and scheduling problems. This paper demonstrates commonly used assembly options, such as metamodeling and machine learning aided initialization, along with some novel ideas like early stopping and cooling control based on neural networks. The evaluation of the results reveals several potential machine learning methods, such as Deep Neural Networks, Hopfield Networks, and Self-Organizing Maps, to assist the metaheuristics. Different training methods for these approaches, including online vs offline training and sources of training data, are also reviewed. Most papers address real-world problems, but there are several intriguing ideas for improving local searches in general. • A P.R.I.S.M.A. review of machine learning assisted metaheuristic methods is presented. • The study focuses on local search hybrids such as Tabu Search and Simulated Annealing. • Several potential machine learning methods are revealed (DNN, Hopfield, SOM, etc.). • Commonly used assembly options, such as metamodeling, are discussed. • Different available training methods, both online and offline, are reviewed. [ABSTRACT FROM AUTHOR]

This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attention in various scenarios and facilitated the development of numerous downstream applications such as urban planning and transportation management. However, the efficacy of existing methods remains sub-optimal due to their tendency to model temporal and spatial relationships independently, thereby inadequately accounting for complex high-order interactions of both worlds. Moreover, the diversity of transitional patterns in traffic forecasting makes them challenging to capture for existing approaches, warranting a deeper exploration of their diversity. Toward this end, this paper proposes Co njoint Spati o -Tempora l graph neural network (abbreviated as COOL), which models heterogeneous graphs from prior and posterior information to conjointly capture high-order spatio-temporal relationships. On the one hand, heterogeneous graphs connecting sequential observation are constructed to extract composite spatio-temporal relationships via prior message passing. On the other hand, we model dynamic relationships using constructed affinity and penalty graphs, which guide posterior message passing to incorporate complementary semantic information into node representations. Moreover, to capture diverse transitional properties to enhance traffic forecasting, we propose a conjoint self-attention decoder that models diverse temporal patterns from both multi-rank and multi-scale views. Experimental results on four popular benchmark datasets demonstrate that our proposed COOL provides state-of-the-art performance compared with the competitive baselines. • We explore a challenging yet practical problem: GNNs for traffic flow prediction. • Our model conjointly explores spatio-temporal relationships from both prior and posterior views. • Our model introduces a conjoint self-attention decoder that aggregates sequential representations. • Our model uses both multi-rank and multi-scale attention branches to learn representations. • Experiments on the benchmarks demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Diseases and pests are essential threat factors that affect agricultural production, food security supply, and ecological plant diversity. However, the accurate recognition of various diseases and pests is still challenging for existing advanced information and intelligence technologies. Disease and pest recognition is typically a fine-grained visual classification problem, which is easy to confuse the traditional coarse-grained methods due to the external similarity between different categories and the significant differences among each subsample of the same category. Toward this end, this paper proposes an effective graph-related high-order network with feature aggregation enhancement (GHA-Net) to handle the fine-grained image recognition of plant pests and diseases. In our approach, an improved CSP-stage backbone network is first formed to offer massive channel-shuffled features in multiple granularities. Secondly, relying on the multilevel attention mechanism, the feature aggregation enhancement module is designed to exploit distinguishable fine-grained features representing different discriminating parts. Meanwhile, the graphic convolution module is constructed to analyse the graph-correlated representation of part-specific interrelationships by regularizing semantic features into the high-order tensor space. With the collaborative learning of three modules, our approach can grasp the robust contextual details of diseases and pests for better fine-grained identification. Extensive experiments on several public fine-grained disease and pest datasets demonstrate that the proposed GHA-Net achieves better performances in accuracy and efficiency surpassing several other existing models and is more suitable for fine-grained identification applications in complex scenes. [ABSTRACT FROM AUTHOR]

This paper proposes a new logical rule by incorporating Random Maximum k Satsifiability in the Hopfield neural network as a single model. The purpose is to combine the optimization capacity of the Hopfield neural network with non-systematic behaviour of the Random maximum k Satisfiability for classification problem. The energy function of a Hopfield neural network has been considered as a programming language for dynamics minimization mechanism. Several optimization and search problems associated with machine learning (ML), decision science (DS) and artificial intelligence (AI) have been expressed on the Hopfield neural network(HNN) optimally by modelling the problem into variables to minimize the objective function corresponding to Lyapunov energy function of the model. The computer simulation has been developed based on RANMAXkSAT logical rule in exploring the feasibility of the Hopfield neural network as a neuro-symbolic integration model for optimal classification problems. The perfromanmce of the proposed hybrid model has been compared with the existing models published in the literature in term of Global minimum ratio (zM), Fitness energy landscapes (FEL), Root Means square error (RMSE), Mean absolute errors and computation time (CPU). Hence, based on the experimental simulation results, it revealed that the RANMAXkSAT can optimally and effectively be represented in the Hopfield neural network (HNN) with 85.1 % classification accuracy. [ABSTRACT FROM AUTHOR]

In this paper, aiming to solve the problem of vital information security as well as neural network application in optical encryption system, we propose an optical image encryption method by using the Hopfield neural network. The algorithm uses a fuzzy single neuronal dynamic system and a chaotic Hopfield neural network for chaotic sequence generation and then obtains chaotic random phase masks. Initially, the original images are decomposed into sub-signals through wavelet packet transform, and the sub-signals are divided into two layers by adaptive classification after scrambling. The double random-phase encoding in 4f system and Fresnel domain is implemented on two layers, respectively. The sub-signals are performed with different conversions according to their standard deviation to assure that the local information's security is guaranteed. Meanwhile, the parameters such as wavelength and diffraction distance are considered as additional keys, which can enhance the overall security. Then, inverse wavelet packet transform is applied to reconstruct the image, and a second scrambling is implemented. In order to handle and manage the parameters used in the scheme, the public key cryptosystem is applied. Finally, experiments and security analysis are presented to demonstrate the feasibility and robustness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In order to identify the uncertain static and dynamic fragile nodes in new energy, the instability and randomness of new energy bring new challenges to the identification of vulnerable nodes in a power grid. Due to the characteristics of low cost and low energy consumption of new energy, people have paid much attention to the exploration and development of new energy. Due to the uncertainty of new energy, it is needed to properly analyze the uncertainty factors. To analyze the uncertainty factors in new energy using the framework of power big data artificial intelligence analysis based on cost-benefit analysis (CBA), it is required to carry out Fourier transform and extract the data characteristic matrix so that a vulnerability risk prediction index can be obtained by using a fuzzy convolution algorithm and binarization, and the safety form between the uncertainty factors in new energy and power stations can be evaluated. In this paper, a fuzzy neural network algorithm is proposed to identify the static and dynamic fragile nodes based on the uncertainty in new energy, so as to ensure the security and stability of the power generation system. The safety performance of the power station system is detected through different levels of early warning sensitivity. The simulation model of the above algorithm is constructed in MATLAB. The simulation results show that the proposed algorithm increases the sensitivity of the early warning system of the power station and the sensitivity of triggering the early warning system and improves the security of the power station system as a whole. [ABSTRACT FROM AUTHOR]

Clustering aims to group n data samples into k clusters. In this paper, we reformulate the clustering problem into an integer optimization problem and propose a recurrent neural network with n × k neurons to solve it. We prove the stability and convergence of the proposed recurrent neural network theoretically. Moreover, clustering experiments demonstrate that the proposed clustering algorithm based on the recurrent neural network can achieve the better clustering performance than existing clustering algorithms. [ABSTRACT FROM AUTHOR]