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In this paper, a novel distributed adaptive neurodynamic approach (DANA) based on proportional integral technique is proposed to solve distributed optimization problem on multi-agent systems. The goal is that all agents reach consensus in finite time and converge to the optimal solution of the global objective function in fixed time. In the proposed approach, the proportional technique drives all agents to reach consensus, and the integral technique is used to offset the influence of the gradient term of the objective function. On the other hand, in order to avoid the prior estimation of gain parameter and the global gradient information, as the main contribution of this paper, the adaptive idea is considered into proportional integral technique. The results show that the adaptive integral technique can automatically adjust the gain according to the maximum consensus error between agents, so as to ensure that agents can achieve consensus in finite time. Then the theoretical results are applied to voltage distribution and logistic regression. Numerical simulation verifies the effectiveness of DANA. [ABSTRACT FROM AUTHOR]

This paper aims to establish several synchronization criteria of competitive complex networks (CCNs) by using fixed-time (FDT) control. In CCNs, the variations of different nodes are diverse if they are influenced by external environment. Here, we design two types of controller to deal with the different variations of nodes. Meanwhile, these designed controllers guarantee the synchronization of the CCNs in a given time. The estimated settling time improves corresponding results in the literature. Furthermore, based on rigorous mathematical proof and the structured comparison system, several FDT synchronization criteria are obtained. Some comparisons are presented to show the advantages of these new theoretical results. The validity of our theoretical results is illustrated by numerical simulations. [ABSTRACT FROM AUTHOR]

To enhance the safety of direct physical interaction between humans and lower-limb exoskeletons, the actuator of the lower-limb exoskeleton should possess compliance, force controllability, and back-drivability. In this paper, a novel compact compliant force control actuator with worm gear and gear rack as transmission modes is designed to provide flexible power to the lower-limb exoskeleton. The worm gear is utilized to amplify the torque generated by the motor in the compact compliant actuator, while the gear rack is established to improve the bearing capacity. Yet, the friction is occurred between the worm gear, rack and other mechanical configurations which may lead to non-negligible force control error of the compliant actuator. A noise-tolerant zeroing neural network controller is proposed to suppress noises. Additionally, theoretical proofs are provided for the convergence performance of the noise-tolerant zeroing neural network controller, as well as the suppression of constant noise, linear disturbances, and bounded random noise. The proposed noise-tolerant zeroing neural network controller is validated on the compliant actuator through numerical simulations, experimental results, and comparison experiments, demonstrating its effectiveness in suppressing various noise and improving the convergence, stability, and robustness of the compliant actuator system. Additionally, the controller is further verified through walking experiments conducted on a knee robot. [ABSTRACT FROM AUTHOR]

This paper studies the quasisynchronization problems of reaction-diffusion neural networks (RDNNs) with time-varying delays via event-triggered control. Firstly, a static event-triggered mechanism and a dynamic event-triggered mechanism are designed to significantly reduce computation costs and save communication resources, respectively. These two different event-triggered control strategies are also able to meet the requirements of various situations. Based on the static event-triggered mechanism, the dynamic event-triggered mechanism is designed to further reduce the sampling frequency by introducing an internal dynamic variable, and several quasisynchronization criteria are derived. However, the quasisynchronization error bounds are related to triggering parameters and can be flexible adjusted, which reduces the conservatism of the existing quasisynchronization results and extends the application of proposed control strategies. Meanwhile, there exists positive lower bounds for the inter event time which can exclude the Zeno behavior. Finally, numerical simulations are given to demonstrate the superiority of the obtained theoretical results, and one example is given to show the chaotic quasisynchronization of the proposed RDNNs in the application of secure communication. [ABSTRACT FROM AUTHOR]

This paper addresses the problem for output synchronization of a class of complex networks (CNs) with reaction-diffusion terms and input constraint. Without considering input constraint, proportional-spatial derivative (P-sD) control of the CN is firstly studied, and then, the problem of output synchronization of CN is formulated as the feasibility problem of linear matrix inequalities (LMIs). Based on the obtained results, considering P-sD control with input constraint, output synchronization is then studied and formulated as the feasibility problem of LMIs and spatial algebraic linear matrix inequalities (SALMIs). In the end, numerical simulation shows the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In this article, the issue of the finite-time anti-synchronization and fixed-time quasi-anti-synchronization for complex-valued neural networks with time-varying delays and leakage delay is explored. First of all, the model is divided into two equivalent real-valued neural networks employing the decomposing technique. Second, by utilizing the Hölder inequality, constructed function and new designed quantized controller, several novel sufficient criteria are obtained to ensure finite-time anti-synchronization of the studied system. In addition, a feedback control strategy is developed to derive some conclusions of quasi-anti-synchronization by applying the Lyapunov function. Meanwhile, the estimated settling time is acquired which does not rely on the initial value. Two methods our paper used to analyze can provide new ideas for future research. Finally, numerical simulation with simulation results is offered to indicate the efficiency and validity of the proposed theoretical results, and the results are applied to the field of image encryption. [ABSTRACT FROM AUTHOR]

In this paper, the optimal formation control issue with collision avoidance and connectivity assurance is investigated for a class of second-order uncertain nonlinear multi-agent systems. First, the neural networks are employed in order to deal with the unknown nonlinear dynamics of the system. Then, an optimal formation control scheme is developed in the framework of the identifier–actor–critic. By constructing a new performance metric function containing collision avoidance and connectivity constraints, it is demonstrated that asymptotic convergence of the tracking error can be achieved under the proposed control scheme. Finally, the effectiveness of the proposed control method is validated by the numerical simulation example. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered fixed-time adaptive neural network formation control method is proposed for underactuated multiple autonomous surface vessels with model uncertainties and unknown external disturbances under communication distance constraints. First, a time-varying barrier Lyapunov function is developed to obtain prescribed performances, such as formation error constraints, and avoid collisions in the communication range with distance limitations. Second, combining backstepping technology with neural networks, a fixed-time adaptive minimum learning parameter (MLP) is proposed to improve robustness against external disturbances and model uncertainties, and an adaptive law is designed to compensate for the approximation error of MLP. Third, a relative threshold-based event-triggered strategy is developed to greatly save communication resources without degrading control performance. Subsequently, Theorem analysis shows that all signals in the closed-loop system are bounded and practical fixed-time stable. Finally, the effectiveness of the proposed method is demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper concerns with the time-variant neural computing in a semi-global sense, taking into account initial conditions located within a region with a definitely finite radius. Both the conventional single and double power-rate RNN models are characterized and the closed-form expressions of the settling time functions are presented for given initial conditions, by which the fixed/predefined-time convergence can be assured in the semi-global sense. Despite asymptotic convergence behavior, the conventional linear RNN model is examined for comparison purposes. Modified RNN models adopt the inverse of the bound, according to the fixed-time convergence results, and the prescribed time can be an adjustable parameter. A novel two-phase RNN model with the pre-specified transition state is proposed, which has not only semi-global fixed/predefined-time stability but also a faster convergence rate than that of the conventional models. The proposed models are applied and compared, through numerical simulation, for time-variant matrix inversion, linear equation solving, and repeatable motion planning of a redundant manipulator in the presence of initial errors. [ABSTRACT FROM AUTHOR]

In this paper, a better fast convergence zeroing neural network (BFCZNN) model with a new activation function (AF) for solving dynamic nonlinear equations (DNE) and applying to control of robot manipulator is presented. The proposed BFCZNN model not only finds the solutions of DNE in fixed time, but also has better robustness than most of the previously reported studies. The numerical simulation results of the proposed BFCZNN and the previously reported robust nonlinear zeroing neural network (RNZNN) for solving third-order DNE in the same condition are presented to demonstrate the better robustness of our new BFCZNN model. Moreover, a successful kinematic control of robot manipulator of our new BFCZNN model is used to verify the realistic availability of the proposed BFCZNN model. [ABSTRACT FROM AUTHOR]

This paper studies the electrical synapse coupling synchronization of Hindmarsh-Rose neurons as well as the effect that Gaussian white noise has on coupling neuron synchronization through theoretical analysis and numerical simulation. The study has found the coupling strength can affect the electrical activity of neuron coupling system and the synchronization status between coupling neurons. Adjusting the coupling strength between the neuron systems can make two asynchronous neuron systems synchronized. The study has also found that proper noise can prompt asynchronous Hindmarsh-Rose neuron electrical synapse coupling to be synchronous and evoke electrical synapse coupling neuron synchronization. [ABSTRACT FROM AUTHOR]

The global synchronization of coupled neural networks with hybrid coupling has been studied in this paper. The hybrid coupling is formed from constant coupling, discrete-delay coupling, and distributed-delay coupling. In this regard, a larger class and more complicated coupled neural networks lead in the synchronization problem procedure. According to the new augmented Lyapunov-Krasovskii functional and the idea of M-segmentation of delay length, a less conservative delay-dependent criterion is obtained and expressed in the form of linear matrix inequalities. In many cases, due to the increasing segmentation number, the delay length M-segmentation method could give an opportunity to the user to find a bigger upper bound of the maximum allowable time delay. The effectiveness of suggested method above is proved by simulating a numerical example on a typical chaotic cellular neural network. The results show that the above-mentioned method is less conservative than the other methods reviewed in this article. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of exponential stability and stabilization for a class of delayed distributed parameter systems, which is modeled by partial integro-differential equations (PIDEs). By employing the vector-valued Wirtinger's inequality, the sufficient condition of exponential stability of the PIDE system with a given decay rate is investigated. The condition is presented by linear matrix inequality (LMIs). After that, we develop a spatial proportional-integral-derivative state-feedback controller that ensures the exponential stabilization of the PIDE system in terms of LMIs. Finally, numerical examples are presented to verify the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive critic neural network controller is designed for a class of discrete-time chaotic system. The critic neural network is used to approximate the long-term function. In contrast with the existing results for discrete-time chaotic systems, in this paper, a near optimal control input can be generated when the long-term function is minimized. It is proven that the tracking error, the adaptation laws and the control input are uniformly bounded. A simulation example is employed to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The existing SRV-MVDR (semi-real-valued MVDR) algorithm is only applicable to the pressure sensors array and cannot distinguish between mirror radiation sources and real source (DOA fuzzy), and this paper presents a method of vector sensors array SRV-MVDR based on subspace projection. Compared with the existing SRV-MVDR, only half spectrum search is needed to solve the DOA fuzzy problem. No subsequent discrimination is needed. The data collected by vector sensors array are processed jointly by using the idea of dimensionality reduction so that it satisfies the processing condition of SRV-MVDR method. Theoretical analysis and computer simulation show that this method has robustness. At the same time, it is more suitable for low SNR and small snapshots and has broad prospects in practical engineering. [ABSTRACT FROM AUTHOR]

Railroad inspections to identify missing track components are crucial to railroad operational safety. This paper presents a new lightweight computer vision model on edge devices for accurate, real-time rail track inspection. It modifies the teacher–student guidance mechanism in NanoDet (https://github.com/RangiLyu/nanodet) by introducing a new adaptively weighted loss (AWL) to the training process. The AWL evaluates the teacher and student model qualities, determines the weight of the student loss, and then balances their loss contributions on-the-fly, gearing the training process toward proper knowledge distillation and guidance. Compared to SOTA models, our AWL-NanoDet features a tiny model size of less than 10 MB and a computation cost of 1.52 G FLOPs and achieves an processing time of less than 14 ms per frame when tested on Nvidia's AGX Orin. Relative to native NanoDet, it also notably improves the model's performance by nearly 10%, enabling highly accurate, real-time detection of track components. [ABSTRACT FROM AUTHOR]

A high-efficiency form-finding algorithm is crucially important for finding a stabilized tensegrity structure. In this paper, a modified Broyden-Fletcher-Goldfarb-Shanno noise-tolerant zeroing neural network (MBFGS-NTZNN) form-finding approach is developed and investigated for the form-finding problems of tensegrity systems. A modified BFGS algorithm (MBFGS) is employed to solve the irreversibility of the Hessian matrix, which could avoid the non-positive definite circumstance of the stiffness matrix. Additionally, the approach could be utilized to make a reduction in algorithm calculation complexity. Moreover, to find a group of suitable nodal coordinates, a zeroing neural network (ZNN) based NTZNN is considered to suppress the noise, which may include rounding errors and external disturbance during the form-finding process. Besides, the 0-stable and global convergence under the pollution of noise are verified. Eventually, numerical simulations and an application example are conducted to ascertain the superiority and availability of the MBFGS-NTZNN algorithm in the fields of form-finding. [ABSTRACT FROM AUTHOR]

This paper investigates the fault estimation problem for a class of nonlinear nonrepetitive systems subject to iteration-dependent references. Firstly, based on the high-order internal model strategy, iterative learning fault estimation scheme is proposed to track the fault signals that varies with iteration index increasing. Then, the convergence of the presented method is achieved by the norm-based approach. Further, the proposed method is also extended to the uncertain systems with varying parameter matrices, discrete-time systems with Lipschitz perturbation and time-variant coefficients. Finally, the effectiveness of the proposed iterative learning fault estimation scheme is verified by numerical simulation studies. [ABSTRACT FROM AUTHOR]

A winner-take-all Lotka-Volterra recurrent neural network with N × N neurons is proposed in this paper. Sufficient conditions for existence of winner-take-all stable equilibrium points in the network are obtained. These conditions guarantee that there is one and only one winner in each row and each column at any stable equilibrium point. In addition, rigorous convergence analysis is carried out. It is proven that the proposed network model is convergent. The conditions for the winner-take-all behavior obtained in this paper provide design guidelines for network implementation and fabrication. Simulations are also presented to illustrate the theoretical findings. [ABSTRACT FROM AUTHOR]

This paper considers the stabilization of stochastic delayed networks with Markovian switching via aperiodically intermittent control (AIC). The concepts of average control ratio and average control period are proposed to characterize the distributions of control and rest intervals of AIC. It should be noted that the averaging technique used here is more general and less restrictive than the quasi-periodicity condition and minimum control ratio condition used in previous works. Then two kinds of stabilization criteria are obtained: (1) the upper bound of time delay should be less than the average control width; (2) the upper bound of time delay has no relationship with the average control width. Finally, the results are applied to studying the stabilization of coupled stochastic neural networks with Markovian switching via AIC. Numerical simulations are provided to show the effectiveness of obtained results. [ABSTRACT FROM AUTHOR]

In previous paper, the number of equilibrium points of a cellular neural network (CNN) was studied. However, the region of different values of equilibrium points of every cell in the CNN is not investigated. Therefore, in this paper, the regions of the number of different values of equilibrium points in cellular neural networks (CNNs) with thresholding activation function are obtained by the relationship between parameters of CNNs. The influence of the values of connection weights of CNNs on the number of equilibrium points for a cell is found. Some sufficient conditions are obtained by using the relationship among connection weights. Depending on these sufficient conditions, inputs and outputs of a CNN, the regions of the values of parameters can be obtained. Finally, some numerical simulations are presented to support the effectiveness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper investigates three-dimensional target tracking control problem of underactuated autonomous underwater vehicles (AUVs) by using coordinates transformation and multi-layer neural networks. The passive-boundedness assumption of sway and heave velocities of underactuated AUVs is used to design a controller in the actuated directions. For this purpose, a new Euler-Lagrange formulation is proposed based on range and bearing tracking errors with respect to a moving target in the body-fixed frame. Then, a tracking controller is proposed to make range and bearing tracking errors converge to zero. Multi-layer neural networks (MLNNs) are utilized to approximate unknown nonlinear-in-parameter dynamics of the system, and adaptive robust control techniques are adopted to compensate for MLNN approximation errors and time-varying environmental disturbances which are induced by waves, wind and ocean currents. The stability of the proposed control system is analysed based on Lyapunov's approach which shows that target tracking errors are semi-globally uniformly ultimately bounded and exponentially tend to a small neighbourhood around the zero. At the end, simulation examples are given to demonstrate the competency of the proposed target tracking controller. [ABSTRACT FROM AUTHOR]

For a class of nonlinear multi-agent systems under switching topologies with disturbances, we propose a distributed H∞ consensus control protocol based on relative output feedback and utilize an iterative algorithm for solving nonlinear matrix inequality in this paper. Firstly, a consensus control protocol via relative output feedback is designed. Then, an iterative algorithm is utilized to calculate nonlinear matrix inequality. By this, the output feedback gain is designed but not chosen, which increases the design degree of freedom and meanwhile H∞ performance index γ is obtained. Finally, the proposed theory is applied to multiple simple-pendulums network systems driven by DC motors, and simulation results show the effectiveness of the designed consensus control protocol. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid observer structure to estimate the lateral tire forces and road grip potential without using any tire-road friction model. The observer consists of an Extended Kalman Filter structure, which incorporates the available prior knowledge about the vehicle dynamics, a feedforward Neural Network structure, which is used to estimate the highly nonlinear tire behavior, and a Recursive Least Squares block, which predicts the road grip potential. The proposed observer was evaluated under a wide range of aggressive maneuvers and different road grip conditions using a validated vehicle model, validated tire model, and sensor models in the simulation environment IPG CarMaker®. The results confirm its good and robust performance. [ABSTRACT FROM AUTHOR]

With the development of crowdsourcing, data acquisition for supervised learning from annotators all over the world becomes simple and economical. To improve accuracy, it is nature to obtain multiple noisy labels (i.e., a multiple label set) for each example from the crowd. Then, consensus algorithms can infer the estimated ground truth from the multiple label set for each example. The estimated ground truth is also called an integrated label, which could be a noise. That is, a dataset constructed via integrating the multiple noisy labels for each example in a crowdsourcing dataset (called an integrated dataset) still contains noises. In order to further improve the data quality of an integrated dataset, so that to improve the performance of a model learned from the integrated dataset, this paper proposes a framework that integrates active learning with the self-healing of a model together. With active learning, a limited number of examples from the integrated dataset, which are most likely noises, are selected for the oracle to correct; with the self-healing of a model, the data quality of the integrated dataset can be also improved automatically. From our experimental results on eight simulated crowdsourcing datasets with three popular consensus algorithms, we draw some conclusions as follows. (1) Our proposed framework does improve the performance of a model learned from the integrated dataset. (2) The simple active learning selection strategy based on uncertainty estimation can identify noises in the integrated dataset. (3) Self-healing is efficient and effective to improve the data quality of the integrated dataset, so that it improves the accuracy of a model learned from the integrated dataset. We further investigate our proposed framework on a real-world crowdsourcing dataset collected from Amazon Mechanical Turk, and the above conclusions are sustained. [ABSTRACT FROM AUTHOR]

This paper investigates a class of non-autonomous cellular neural networks with mixed delays. Based on the basic theory of the weighted pseudo-almost periodic functions, several sufficient conditions are established to ensure that every solution of the addressed model exponentially tends to a weighted pseudo-almost periodic solution as t→+∞, which generalize some existing ones. In particular, some numerical examples are also given. [ABSTRACT FROM AUTHOR]

Twin support vector machine (TWSVM) is a research hot spot in the field of machine learning in recent years. Although its performance is better than traditional support vector machine (SVM), the kernel selection problem still affects the performance of TWSVM directly. Wavelet analysis has the characteristics of multivariate interpolation and sparse change, and it is suitable for the analysis of local signals and the detection of transient signals. The wavelet kernel function based on wavelet analysis can approximate any nonlinear functions. Based on the wavelet kernel features and the kernel function selection problem, wavelet twin support vector machine (WTWSVM) is proposed by this paper. It introduces the wavelet kernel function into TWSVM to make the combination of wavelet analysis techniques and TWSVM come true. The experimental results indicate that WTWSVM is feasible, and it improves the classification accuracy and generalization ability of TWSVM significantly. [ABSTRACT FROM AUTHOR]

The number of applications running over computer networks has been increasing tremendously, which increased the number of packets running over the network as well leading to resource contention, which ultimately results in congestion. Congestion increases both delay and packet loss while reducing bandwidth utilization and degrading network performance. Network congestion can be controlled by several methods, such as random early detection (RED), which is the most well-known and widely used method to alleviate problems caused by congestion. However, RED and its variants suffer from linearity and parametrization problems. In this paper, we proposed a new method called fuzzy logic RED (FLRED), which extends RED by integrating fuzzy logic to overcome these problems. The proposed FLRED method relies on the average queue length (aql) and the speculated delay (DSpec) to predict and avoid congestion at an early stage. A discrete-time queue model is used to simulate and evaluate FLRED. The results showed that FLRED outperformed both RED and effective RED (ERED) by decreasing both delay and packet loss under heavy congestion. Compared with ERED and RED, FLRED decreased the delay by up to 1.5 and 4.5% and reduced packet loss by up to 6 and 30%, respectively, under heavy congestion. These findings suggest that FLRED is a promising congestion method that can save network resources and improve overall performance. [ABSTRACT FROM AUTHOR]

This paper develops an adaptive neuro-fuzzy inference system-based grey time-varying sliding mode control (TVSMC) for the application of power conditioning systems. The presented methodology combines the merits of TVSMC, grey prediction (GP), and adaptive neuro-fuzzy inference system (ANFIS). Compared with classic sliding mode control, the TVSMC accelerates reaching phase and guarantees the sliding mode existence starting at arbitrary primary circumstance. But, as a highly nonlinear loading occurs, the TVSMC will undergo chattering and steady-state errors, thus degrading PCS’s performance. The GP is therefore used to attenuate the chattering if the overestimate of system uncertainty bounds exists and to lessen steady-state errors if the underestimate of system uncertainty bounds happens. Also, the GP-compensated TVSMC control gains are optimally tuned by the ANFIS for achieving more precise tracking. Using the proposed methodology, the power conditioning system (PCS) robustness is increased expectably, and low distorted output voltage and fast transient response at PCS output can be achieved even under nonlinear loading. The analysis in theory, design process, simulations, and digital signal processing-based experimental realization for PCS are represented to support the efficacy of the proposed methodology. Because the proposed methodology is easier to implement than prior methodologies and provides high tracking accuracy and low computational complexity, the contents of this paper will be of interest to learners of correlated artificial intelligence applications. [ABSTRACT FROM AUTHOR]

In this paper, a prescribed performance adaptive neural network synchronization is investigated for a class of unknown chaotic systems in the presence of input saturation and external unknown disturbances. A prescribed performance function is employed to transform the constraint problem of chaotic synchronization control error into the problem of guaranteeing the boundedness of the transformed error. By introducing the Gaussian error function, the input saturation is handled. A neural network-based synchronization control scheme is then developed. Under the developed synchronization control scheme, the synchronization of uncertain chaotic systems is achieved with different initial conditions. Numerical simulation results further demonstrate the effectiveness of the proposed synchronization control scheme for unknown chaotic systems subject to external unknown disturbances and input saturation. [ABSTRACT FROM AUTHOR]

This paper concerns the input-to-state stability (ISS) problems for a class of discrete-time nonlinear input-saturated switched descriptor systems (SDSs). An ISS criterion that only partial subsystems are exponentially stable is provided based on average dwell time method and discrete-time iterative algorithm. The proof difficulty is greatly decreased, and the switching controllers for the subsystems of the closed-loop SDSs are much simple and viable. Furthermore, the cost of the controllers is also greatly reduced. Finally, extensive simulation results are presented to illustrate the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to study the dynamics of a delayed inertial neural networks with rectangular input currents from the asymptotic point of view. Under certain circumstances, we studied the existence, the uniqueness of bounded solutions and their homoclinic and heteroclinic motions. Moreover, for the demonstration of the theoretical results, we give an example and its numerical simulations. [ABSTRACT FROM AUTHOR]

This paper presents an optimal design of low noise amplifier (LNA) using an efficient swarm-based optimizer called firefly algorithm (FA). Many researchers have used firefly algorithm to solve various nonlinear engineering problems and reported outstanding results. In view of this, FA is implemented for the first time in this paper to optimize the parameters of LNA like gain and noise figure (NF). Two case studies have been performed which includes the minimization of NF and maximization of gain. Optimization of these two parameters has been carried out by considering each parameter as a single objective function. Penalty factor method is considered for handling the constraints. Other parameters of LNA like power consumption, linearity, and stability are also discussed for both the cases. The designed LNA has a cascode structure with inductive source degeneration topology and is implemented in UMC 0.18-μm CMOS technology using CADENCE software. LNA is designed for 5.5-GHz frequency. The performance of FA in optimizing the parameters of LNA is also compared with the performance of other similar contemporary algorithms like particle swarm optimization (PSO), human behavior PSO (HB-PSO), backtracking search algorithm, and cuckoo search algorithm (CSA). The optimized value of LNA parameter using FA and other algorithms when simulated in MATLAB environment is compared with the simulated result of CADENCE. Statistical analysis is also performed for each case study, and the results are compared with the above-mentioned optimization algorithms. Simulation results, comparative study, and statistical analysis confirm the superiority of FA over other methods in terms of its computational efficiency, consistency, and robustness. [ABSTRACT FROM AUTHOR]

In today's ever-changing and highly uncertain environment, organizations depend on research and development (R&D) activities to adapt intensive growth of technology. One of the most important and trickiest tasks of any developing firms is to define new projects. This process gets difficult when it comes to choosing an appropriate portfolio from a set of candidate projects. Since organizations are faced with limited resources of R&D and budget constraints, they have to choose a project portfolio that mitigates the corresponding risk and enhances the overall value of portfolio. Therefore, the purpose of this study is to introduce a practical model to select the best and the most proper project portfolio while considering project investment capital, return rate, and risk. The ever-changing and highly uncertain environment of projects is addressed by utilizing interval type-2 fuzzy sets (IT2FSs). In this paper, a new model of R&D project evaluation is first introduced. This model includes a new risk-return index. This model is then extended in project portfolio selection, and as a result, a new model of R&D project portfolio selection is proposed under uncertainty. Constraints and limitations of R&D project portfolio selection are comprehensively addressed. In this model, lower semi-variance is applied to consider risk of proposed projects. Therefore, this paper offers a new model that applies IT2FSs to handle uncertainty, uses semi-variance to assess risk, synchronously considers risk and return in its selection process, and addresses the considerations and limits of real-world problems. Eventually, to verify the proposed model, a numerical example of the existing literature is solved with the model, and the results are compared. The first proposed model is used to prioritize proposed R&D projects of a gas and oil development holding firm as a real case study. To illustrate further, a practical example is also provided to demonstrate the applicability of the proposed project portfolio selection model. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive robust finite-time neural control scheme is proposed for uncertain permanent magnet synchronous motor servo system with nonlinear dead-zone input. According to the differential mean value theorem, the dead zone is represented as a linear time-varying system, and the model uncertainty including the dead zone is approximated by using a simple neural network. Then, an adaptive finite-time controller is designed based on a fast terminal sliding mode control principle, and the singularity problem in the initial TSMC is circumvented by modifying the terminal sliding manifold. Comparative experiments are conducted to validate the effectiveness and superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the lag quasi-synchronization for memristive neural networks (MNNs) with switching jumps mismatch. The inherent characteristic of MNNs is fully taken into account. Based on Lyapunov-Krasovskii functional and differential inclusions theory, intermittent control approach is utilized to realize the exponential lag quasi-synchronization of the considered model. The error level is closely related to the switching jumps. In addition, a simple design procedure of controller is presented to ensure that the synchronization error between the master system and the slave system converges to a predetermined level. Two numerical examples are offered to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an asymptotically stable optimal control is proposed for the trajectory tracking of a cylindrical robotic arm. The proposed controller uses the linear quadratic regulator method and its Riccati equation is considered as an adaptive function. The tracking error of the proposed controller is guaranteed to be asymptotically stable. A simulation shows the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In reality, manufacturers motivate sales agents to increase promotional effort with the sales rebate and penalty contract, the agents always concern about the fairness, and information flow structure among agents is a small-world network. In order to improve incentive performance under this situation, this paper builds a multi-agent model mainly to observe the impact of small-world network characteristics on incentive effects. Experimental results show that greater probability of replacement comparison objects will induce lower profit and products sales volume of the manufacturer; agents will have a huge negative impact on incentive effects when they get related information from a very small amount of agents. The aim of this paper is to provide suggestions for manufacturers to utilize the characteristic of small-world network rational so as to improve the incentive effect. [ABSTRACT FROM AUTHOR]

This paper studies the problem of underdetermined blind source separation with the nonstrictly sparse condition. Different from current approaches in literature, we propose a new and more effective algorithm to estimate the mixing matrices resulted from noise output data sets. After we introduce a clustering prototype of orthogonal complement space and give an extension of the normal vector clustering prototype, a new method combing the fuzzy clustering and eigenvalue decomposition technique to estimate the mixing matrix is presented in order to deal with the nonstrictly sparse situation. A convergent algorithm for estimating the mixing matrices is established, and numerical simulations are given to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper studies projective lag synchronization of coupled neural networks with time delay and parameter mismatch. An adaptive controller is designed to achieve weak projective lag synchronization of coupled neural networks. This method is employed to realize projective lag synchronization between coupled neural systems with an error level. Numerical simulation illustrates the effectiveness of the results. [ABSTRACT FROM AUTHOR]

Quality of service (QoS) model of composite services and web service selection based on QoS are currently the hot issues in the web service composition area. Service selection based on QoS, which is a globally optimal selection issue, is a NP-hard problem. Taking engine into consideration, this paper develops a QoS model for service selection in the web composite services. We use the algorithm on the estimation of distribution to solve the NP-hard problem of service selection and present a web service selection method based on the univariate marginal distribution algorithm (UMDA). Simulation analysis and experimental study based on the UMDA method are carried out. It is proved that the method is effective in solving the NP-hard problem. [ABSTRACT FROM AUTHOR]

In this paper, a class of fuzzy cellular neural networks with time-varying delays is considered. Based on the almost periodic function theory, by applying differential inequality techniques, we establish some sufficient conditions for the existence and exponential stability of almost periodic solutions for the model, which are new and complement with some recent ones. An example and its numerical simulations are presented to demonstrate the feasibility and effectiveness of the main results. [ABSTRACT FROM AUTHOR]

Two-dimensional system model represents a wide range of practical systems, such as image data processing and transmission, thermal processes, gas absorption and water stream heating. Moreover, there are few dynamical discussions for the two-dimensional neutral-type Cohen-Grossberg BAM neural networks. Hence, in this paper, our purpose is to investigate the stability of two-dimensional neutral-type Cohen-Grossberg BAM neural networks. The first objective is to construct mathematical models to illustrate the two-dimensional structure and the neutral-type delays in Cohen-Grossberg BAM neural networks. Then, a sufficient condition is given to achieve the stability of two-dimensional neutral-type continuous Cohen-Grossberg BAM neural networks. Finally, simulation results are given to illustrate the usefulness of the developed criteria. [ABSTRACT FROM AUTHOR]

In this paper, consensus problems of multi-agent systems with nonlinear dynamics and time delays are investigated. Two cases are considered that are the system without a leader and the one with a leader. New linear control protocols are introduced to make second-order multi-agent systems consensus. By applying the Lyapunov function method and the graph theory, some sufficient conditions for consensus of positions and velocities are derived. Finally, two simulation results are given to illustrate the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

This paper illustrates the application of an adaptive flight control architecture to a scale quad-rotor. For autonomous vertical takeoff and landing flight, it is common to separate the control problem into an inner fast loop that controls attitude and an outer slow loop that controls the trajectory tracking. In this paper, we augment a conventional proportional and derivative controller conceived mainly for hovering, with an adaptive element using a real-time tuning single hidden layer neural network in a inner–outer loop combined architecture to account for model inversion error cancelation, issued in the feedback linearization process. The results shown in simulations reveal the superior performance of the augmented controller in tracking maneuvers. [ABSTRACT FROM AUTHOR]

In this paper, we reconsider and analyze our previous paper a novel hash algorithm construction based on chaotic neural network, then present equal-length and unequal-length forgery attacks against its security in detail, and then propose a significantly improved approach by utilizing a method of complicated nonlinear computation to enhance the security of the original hash algorithm. Theoretical analysis and computer simulation indicate that the improved algorithm can completely resist the two kinds of forgery attacks and also shows other better performance than the original one, such as better message and key sensitivity, statistical properties, which can satisfy the performance requirements of a more secure hash function. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of exponential synchronization of switched stochastic competitive neural networks (SSCNNs) with both interval time-varying delays and distributed delays. The distributed delays can be unbounded or bounded; the stochastic perturbation is of the form of multi-dimensional Brownian motion, and the networks are governed by switching signals with average dwell time. Based on new multiple Lyapunov-Krasovkii functionals, the free-weighting matrix method, Newton-Leibniz formulation, as well as the invariance principle of stochastic differential equations, two sufficient conditions ensuring the exponential synchronization of drive-response SSCNNs are developed. The provided conditions are expressed in terms of linear matrix inequalities, which are dependent on not only both lower and upper bounds of the interval time-varying delays but also delay kernel of unbounded distributed delays or upper bounds for bounded distributed delays. Control gains and average dwell time restricted by given conditions are designed such that they are applicable in practice. Numerical simulations are given to show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

By the definition of piecewise almost periodic functions, this paper investigates the existence and stability of a discontinuous almost periodic solution of the impulsive Hopfield neural networks with finite distributed delays and periodic impulses, using fixed point theorems, Lyapunov functional, and some inequality techniques. Compared with known relevant results mainly concentrating on continuous neural networks, the obtained criteria consider impulse effects. Moreover, it does not use Cauchy matrix, and the corresponding hypotheses about linear impulse systems which are often used in the reference. The obtained criteria are easily applicable and checkable, and an illustrative example with simulations shows this. Furthermore, the proposed results are applicable to some other networks with periodic impulses. [ABSTRACT FROM AUTHOR]

Different from gradient-based neural dynamics, a special kind of recurrent neural dynamics has recently been proposed by Zhang et al. for solving online time-varying problems. Such a recurrent neural dynamics is designed based on an indefinite error-monitoring function instead of a usually norm- or square-based energy function. In addition, Zhang neural dynamics (ZND) are depicted generally in implicit dynamics, whereas gradient-based neural dynamics (GND) are associated with explicit dynamics. In this paper, we generalize the ZND design method to solving online nonlinear time-varying equations in the form of f ( x, t) = 0. For comparative purposes, the GND model is also employed for such time-varying equations' solving. Computer-simulation results via power-sigmoid activation functions substantiate the theoretical analysis and efficacy of the ZND model for solving online nonlinear time-varying equations. [ABSTRACT FROM AUTHOR]

The present paper solves the approximation problem of T–S fuzzy linear singular system for a class of nonlinear singular system with impulses. Consider a special nonlinear singular bio-economic system with impulses; the T–S fuzzy linear singular system of the nonlinear singular system has been calculated. The relationship between the impulse of the singular system and the singular induced bifurcation is proved for the first time. For this particular case, it is extended to more generally nonlinear singular system. For a class of nonlinear singular system that is bounded impulse-free item and separable impulse item with singularity-induced bifurcation, we proved that it can be approximated by T–S fuzzy singular system with arbitrary accuracy. Finally, a numerical simulation is carried out to show the consistency with theoretical analysis and illustrate the effectiveness of approximation. [ABSTRACT FROM AUTHOR]