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In this paper, a robust H ∞ design approach is proposed to accomplish the state estimation task for a class of continuous-time gene regulatory networks (GRNs) in the finite-time framework. For the GRNs under investigation, the distributed inter-node time delays and intra-node time delays are, respectively, taken into account in the feedback regulation function and the translation process. Moreover, the parameter uncertainty with convex polytopic constraint is introduced to characterize the unavoidable modeling errors induced by parameter sensitivity, calibration accuracy, abrupt structural changes and failures, etc. The primary objective of this paper is to design a robust state estimator for the considered GRNs based on the measured concentrations of messenger ribonucleic acid and protein such that the finite-time boundedness and the H ∞ performance are guaranteed. By resorting to the Lyapunov stability theory, the Jensen's inequality and the Gronwall inequality, some sufficient conditions are established in terms of linear matrix inequalities. Subsequently, a set of parameter matrices with special structures is designed by means of the convex optimization techniques. Finally, a numerical example is provided to demonstrate the finite-time boundedness and the robust performance of the developed state estimation approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the finite-time state estimation problem for a class of delayed artificial neural networks under the stochastic communication protocol. The underlying time delay is time-varying yet bounded. Compared with the common-used sigmoid-type nonlinearity, a more general type of nonlinearity is adopted to describe the neuron activation function and the nonlinearity of the measurement output, respectively. In order to avoid the communication collision, the stochastic communication protocol is introduced between the transmitter and the receiver, and the corresponding scheme is characterised with the help of a Markov chain. By introducing an auxiliary vector, a novel state estimator structure is proposed. The stochastic finite-time stability of the error dynamics is first analyzed via the stochastic analysis techniques and the Lyapunov stability theory, and then the sufficient condition for the existence of the desired state estimator is obtained. Subsequently, the estimator gain is parameterized by using a set of easy-to-check computational condition. Finally, a numerical example is provided to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel dual-pathway-fusion-based sequence-to-sequence learning model (DPF-S2S) is proposed for text recognition in the wild, which mainly focuses on enriching the spatial information and extracting high-dimensional representation features to assist decoding. In particular, a double alignment module is developed to solve the problem of text misalignment, where both position and vision information are well considered. Moreover, a global fusion module is deployed to enrich 2D information in the aligned attention maps, which benefits accurate recognition from complicated scenes with arbitrary text shapes and poor imaging conditions. Benchmark evaluations on seven datasets have demonstrated the superiority of proposed DPF-S2S model in comparison to other state-of-the-art text recognition methods, which presents great competitiveness on identifying texts in both regular and irregular scenes. In addition, extensive ablation studies have been carried out, which validate the effectiveness of applied strategies in proposed DPF-S2S. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stability issue of quaternion-valued neural networks with neutral delay, proportional delay and leakage delay. Taking use of the principle of homeomorphism, techniques of matrix inequality and Lyapunov stability theory, a main stability criterion is derived in the form of quaternion-valued linear matrix inequality for ensuring the unique existence and global stability of the equilibrium point for the considered quaternion-valued neural networks. An illustrative example and its simulations are given to show the effectiveness of the theoretical result. [ABSTRACT FROM AUTHOR]

This paper deals with the state estimate problem for a class of discrete-time complex networks. The target plant involves Markovian jumping parameters and mode-dependent distributed time-delays. It is assumed that the measurements for the estimate purpose are just from a portion of network nodes, and corrupted by a certain stochastic noise. A unified framework is developed to cope with the state estimation issue based on the outputs from the fraction of nodes for the discrete-time complex networks with distributed time delay and measurement noise. By employing the Lyapunov stability theory and some new techniques, the sufficient conditions are established to ensure the state estimation error is exponentially ultimately mean-square bounded. As the special case when the measurement is noise-free, the resulting criteria are to guarantee that the dynamics of state estimation error is exponentially stable. In addition, the estimator gain matrices are given in the explicit formula. Finally, numerical simulations are presented to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the dynamic event-triggered state estimation problem for a class of spatial-temporal networks (STNs) with time-varying delays under an encoding-decoding strategy. For the sake of reducing the unnecessary resources wastes, we establish a dynamic event-triggered mechanism to determine whether the current measurement output data is transmitted to the filter, where the threshold is dynamically adjusted according to a certain rule. In order to enhance the robustness of signal transmission, an encoding-decoding strategy is exploited in the process of the data transmission. To be specific, the original signals encoded as a bit string are transmitted through binary symmetric channels with certain crossover probabilities and then restored by a decoder at the receiver. By constructing Lyapunov-Krasovskii functional, we obtain a sufficient condition to ensure that the estimation error system is exponential mean square ultimately bounded. Subsequently, the desired state estimator is designed in terms of the solution to a certain matrix inequality. Finally, a numerical example is shown to demonstrate that the proposed state estimator is valid for time-delayed STNs. [ABSTRACT FROM AUTHOR]

In this paper, the stochastic quaternion-valued neural networks model with variable coefficients and neutral delays is considered, and the mean-square stability criterion is provided via the method of mathematical analysis. In deriving stability criterion, the considered stochastic quaternion-valued neural networks model is implemented as an entirety form without separating the model into two complex-valued or four real-valued models. And the obtained result is valid for stochastic real-valued and complex-valued neural networks. A numerical simulation example is given to show the effectiveness of the obtained result. [ABSTRACT FROM AUTHOR]

In this paper, the issue of input-to-state exponential stability (ISES) for stochastic complex-valued neural networks with neutral delay (SCVNNs) and discrete delay is considered. Without separating the SCVNNs into two real-valued systems, two criteria expressed through linear matrix inequality (LMI) to pledge ISES of the considered SCVNNs are derived based on Itô formula in complex-valued field, Lyapunov–Krasovskii functional approach as well as some relevant inequality skills. Two examples are furnished to verify the raised results. [ABSTRACT FROM AUTHOR]

The stability of fractional-order complex-valued neural networks (FOCVNNs) with probabilistic time-varying delays is investigated in this paper. By constructing suitable Lyapunov–Krasovskii functional and utilizing inequality technique, a complex-valued linear matrix inequality (LMI) criterion guaranteeing the global asymptotic stability of the proposed FOCVNNs is deduced. A numerical example with simulations is provided to demonstrate the feasibility and availability of the obtained theoretical result. [ABSTRACT FROM AUTHOR]

This paper is devoted to researching the robust stability of fractional-order complex-valued projective neural networks (FOCVPNNs) with neutral-type delays and uncertain parameters. Without dividing the FOCVPNNs into two real-valued systems, based on Lyapunov method, matrix inequality technique and homeomorphism principle, several delay-independent and delay-dependent criteria are established to make sure that the equilibrium point of the addressed FOCVPNNs is existent, unique and globally robustly stable. Finally, three examples with simulations are given to verify the availability of the main results. [ABSTRACT FROM AUTHOR]

In this paper, a competitive mechanism integrated whale optimization algorithm (CMWOA) is proposed to deal with multi-objective optimization problems. By introducing the novel competitive mechanism, a better leader can be generated for guiding the update of whale population, which benefits the convergence of the algorithm. It should also be highlighted that in the competitive mechanism, an improved calculation of crowding distance is adopted which substitutes traditional addition operation with multiplication operation, providing a more accurate depiction of population density. In addition, differential evolution (DE) is concatenated to diversify the population, and the key parameters of DE have been assigned different adjusting strategies to further enhance the overall performance. Proposed CMWOA is evaluated comprehensively on a series of benchmark functions with different shapes of true Pareto front. Results demonstrate that proposed CMWOA outperforms other three methods in most cases regarding several performance indicators. Particularly, influences of model parameters have also been discussed in detail. At last, proposed CMWOA is successfully applied to three real world problems, which further verifies the practicality of proposed algorithm. [ABSTRACT FROM AUTHOR]

Gold immunochromatographic strip (GICS) is a widely used lateral flow immunoassay technique. A novel image segmentation method is developed in this paper for quantitative analysis of GICS based on the deep reinforcement learning (DRL), which can accurately distinguish the test line and the control line in the GICS images. The deep belief network (DBN) is employed in the deep Q network in our DRL algorithm. Meanwhile, the multi-factor learning curve is introduced in the DRL algorithm to dynamically adjust the capacity of the replay buffer and the sampling size, which leads to enhanced learning efficiency. It is worth mentioning that the states, actions, and rewards in the developed DRL algorithm are determined based on the characteristics of GICS images. Experiment results demonstrate the feasibility and reliability of the proposed DRL-based image segmentation method and show that the proposed new image segmentation method outperforms some existing image segmentation methods for quantitative analysis of GICS images. [ABSTRACT FROM AUTHOR]

This paper is concerned with the event-triggered state estimation (ETSE) problem for a class of discrete-time Markovian jumping neural networks with mode-dependent time-delays and uncertain transition probabilities. The parameters of the neural networks experience switches that are characterized by a Markovian chain whose transition probabilities are allowed to be uncertain. The event-triggered mechanism is introduced in the sensor-to-estimator channel to reduce the frequency of signal communication. The aim of this paper is to develop an ETSE scheme such that the estimation error dynamics is exponentially ultimately bounded in the mean square. To achieve the aim, two sufficient conditions are proposed with the first one guaranteeing the existence of the required state estimator, and the second one giving the algorithm for designing the corresponding estimator gain by solving some matrix inequalities. In the end, the validity of the proposed estimation scheme is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

This paper focuses on the robust stability analysis of fractional-order quaternion-valued neural networks (FOQVNNs) with neutral delay and parameter uncertainties. Without transforming the FOQVNNs into equivalent two complex-valued systems or four real-valued systems, based on homeomorphism principle, matrix inequality technique and Lyapunov method, both delay-independent and delay-dependent criteria to guarantee the existence, uniqueness and global stability of equilibrium point for the considered FOQVNNs are derived in the form of linear matrix inequality (LMI). Two examples with simulations are provided to manifest the theoretical results. [ABSTRACT FROM AUTHOR]

Stability problem of quaternion-valued neutral-type delayed neural networks (QVNTDNNs) is researched in this paper. By utilizing homeomorphism principle, matrix inequality technique and Lyapunov method, both delay-independent and delay-dependent stability criteria are established in the form of linear matrix inequalities to pledge the existence, uniqueness and global stability of the equilibrium point of the considered QVNTDNNs. An example with simulations is given to manifest the validity of the exported results. [ABSTRACT FROM AUTHOR]

The asymptotic stability and synchronization for nonlinear distributed-order system with uncertain parameters is investigated in this paper. To this end, a new lemma for the distributed-order system is proposed to certify the asymptotic stability. Several sufficient conditions are presented to acquire the asymptotic stability and synchronization for such distributed-order models. In addition, by designing two numerical examples, the feasibility and effectiveness of theoretical results are verified. [ABSTRACT FROM AUTHOR]

In this paper, the robust stability of uncertain fractional order singular systems with neutral and time-varying delays is investigated. By applying Lyapunov–Krasovskii functional approach, several sufficient conditions in the form of linear matrix inequality to ensure asymptotical stability and robust stability are derived for the considered systems. The advantage of the employed method in this paper is that one may directly calculate integer-order derivatives of Lyapunov–Krasovskii functional. Several simple examples are given to illustrate the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

This paper gives a review of recent advances on memristive neural networks with emphasis on the issues of stability analysis and state estimation. First, the concept of memristive neural network is recalled with a brief introduction of its background. Then, certain types of frequently seen neural networks are reviewed comprehensively with latest progress. Some engineering-oriented phenomena that appear extensively in the context of networked systems are introduced and summarized, including random dynamics, time-delays and network-induced incomplete information, etc. From different perspectives, several techniques explored for designing the required state estimators of memristive neural networks are discussed in detail. Some latest progress regarding the stability analysis and state estimation problems for discrete time memristive neural networks are presented. Finally, we provide the conclusions and point out certain future research directions. [ABSTRACT FROM AUTHOR]

In this paper, the problem of event-triggered non-fragile state estimator design for discrete-time delayed neural networks (DNNs) is investigated over finite-time span. In consideration of the changes of environment and high sensitivity, external disturbances and/or parameter uncertainties might be involved in estimator parameters of the concerned DNNs. Therefore, it is one of our main objectives to design a non-fragile state estimator subject to the norm bounded gain variation. The sensor nonlinearity is supposed to occur in a random way. In the meanwhile, the event-triggered scheme and energy constraints are adopted in state estimator design for the purpose of energy and resource saving. By using the Lyapunov stability theory and some analytical techniques, sufficient conditions are established to guarantee that the estimation error system is finite-time bounded and meet a prescribed mixed H ∞ and passivity performance constraint. Furthermore, the estimator gains are obtained via solving a set of linear matrix inequalities (LMIs). Finally, two numerical examples are exploited to demonstrate the effectiveness of the developed technique. [ABSTRACT FROM AUTHOR]

In this paper, the periodicity of a class of impulsive Cohen–Grossberg-type fuzzy neural networks with hybrid delays is investigated. By applying integro-differential inequality with delays, the Lyapunov function, the Poincare map and the fixed point theory, we obtained some new sufficient conditions which guarantees the existence of periodic solutions and global exponential stability of the formulated system. Three numerical examples are given to show the effectiveness and generality of our method. [ABSTRACT FROM AUTHOR]

An adaptive finite-time dynamic surface tracking control scheme is concentrated for a class of nonaffine nonlinear systems with dead zone. By virtue of Lyapunov theory of finite time stability and backstepping technology, an adaptive fuzzy finite-time dynamic surface tracking controller is correctly devised, in which fuzzy logic systems (FLSs) are adopted to approximate the unknown terms. Different from the usual dynamic surface control strategy, the presented approach combines the finite-time control with this scheme by using backstepping technique, which not only eliminates the problem of explosion of complexity, but also ensures that all the signals of the closed-loop system are semi-globally practically finite-time stable, meanwhile, the tracking error can be confined to a small region near the equilibrium point. At last, a numerical example is given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the global asymptotical synchronization of two nonidentical complex-valued neural networks (CVNNs) with leakage delay and time-varying delays via integral sliding mode control approach. A suitable sliding surface and an appropriate Lyapunov–Krasovskii functional are constructed. By using free-weighting matrix method combined with inequality technique, a sliding mode controller is designed to ensure the synchronization for the considered CVNNs. The obtained criterion is presented in the form of complex-valued linear matrix inequalities (LMIs), which could be calculated through YALMIP with solver of SDPT3 in MATLAB. An example is provided to demonstrate the obtained theoretical result. [ABSTRACT FROM AUTHOR]

Abstract This paper focus on the synchronization of complex-valued neural networks (CVNNs) with both discrete and distributed two additive time-varying delays. By applying matrix inequality technique and exploiting reciprocally convex approach, several delay-dependent criteria are presented in the form of linear matrix inequalities (LMIs) to ensure the global synchronization of CVNNs via structuring an appropriate Lyapunov–Krasovskii functional. An example with simulations is provided to ensure the feasibility of the obtained result. [ABSTRACT FROM AUTHOR]

Abstract The periodicity problem of a class of Cohen–Grossberg-type fuzzy neural networks with impulses and time-varying delays is concerned in this paper. Via constructing a delay differential inequality, and applying fuzzy theory and the Lyapunov method, several criteria which ensure the existence and exponential stability of the periodic solutions for the considered systems are derived. An example is shown to illustrate the validity of the obtained results. [ABSTRACT FROM AUTHOR]

Abstract In this paper, an adaptive neural-network-based tracking control strategy is proposed for a class of nonlinear switched non-lower triangular systems with the completely unknown nonlinearities and unmodeled dynamics. The design difficulties arise mainly from the fact that the intercoupling between the non-lower triangular functions and the unmodeled dynamics leads the switched system under consideration has a very complex structure. In order to get the desired adaptive state feedback controller, a mild assumption associated with a dynamic signal is utilized to deal with the unmodeled dynamics, and a separating variable method is presented to handle the system nonlinearities with all state variables in the framework of adaptive backstepping technique, respectively. The obtained results show that all signals of the switched closed-loop system are semi-global bounded with the output tracking error can be guaranteed to enter a small region around the origin. In the end, two simulation examples are given to demonstrate the feasibility and practicability of the presented design strategy. [ABSTRACT FROM AUTHOR]

Abstract This article mainly concerns the synchronization in finite-time to the time-varying delay fractional order memristive competitive neural networks (TMFCNNs) with leakage delay. By means of Fillipov’s theory, Gronwall–Bellman integral inequality, H o ¨ lder’s inequality, and the Caputo derivative properties, the novel algebraic sufficient conditions are proposed to guarantee the synchronization in finite time of addressing TMFCNNs with non-integer order: 0 < p < 1 2 and 1 2 ≤ p < 1 via finite-time output feedback controller. Up to now, there are no relevant results in fractional order competitive-type neural networks, and this article makes filling up for this gap. The obtained results are improved to some existing results on integer-order memristive competitive neural networks. Finally, two numerical examples with simulations are also designed to confirm the merits of the proposed theoretical results, while we estimate the upper bound of the settling time for the synchronization errors. [ABSTRACT FROM AUTHOR]

Abstract This work considers robust adaptive neural-based control of pure-feedback stochastic nonlinear systems with the generalized Prandtl–Ishlinskii hysteresis. The mean-value theorem is employed to handle the non-affine difficulties from the generalized Prandtl–Ishlinskii hysteresis and the pure-feedback systems. By using the radial basis function (RBF) neural networks’ universal approximation capability and backstepping technique, an adaptive neural control scheme with minimum adaptive parameter is developed. The presented controller can guarantee the semi-global boundedness in fourth-moment of all signals of the resulting closed-loop system. Furthermore, the system output is ensured to converge to a small domain of the given trajectories. Simulation results are presented to demonstrate the effectiveness of the scheme. [ABSTRACT FROM AUTHOR]

This paper is concerned with the open-circuit fault diagnosis of phase-controlled three-phase full-bridge rectifier by using a sparse autoencoder-based deep neural network (SAE-based DNN). Firstly, some preliminaries on SAE-based DNN are briefly introduced to automatically learn the representative fault features from the raw fault signals. Then, a novel strategy is developed to design the structure of the SAE-based DNN, by which the depth and hidden neurons of the SAE-based DNN could be regularly determined to extract the features of input signals. Furthermore, the fault model and system framework are presented to diagnose the open-circuit fault of the three-phase full-bridge rectifier. Finally, the effectiveness of the developed novel strategy is verified by the results of simulation experiments, and the superiority of the novel SAE-based DNN is evaluated by comparing with other frequently used approaches. [ABSTRACT FROM AUTHOR]

In this paper, the robust H ∞ state estimation problem is investigated for a class of discrete-time delayed bidirectional associative memory neural networks with randomly occurring uncertainties (ROUs) and sensor saturations. In order to more clearly reflect the reality in a networked environment, Bernoulli distributed white sequences with known probabilities are introduced to characterize the network-induced phenomena of the ROUs and the randomly occurring sensor saturations. The purpose of the addressed problem is to design a robust H ∞ state estimator such that the dynamics of the estimation error is asymptotically stable in the mean square. By using matrix computation and stochastic analysis approaches, a sufficient condition is established to ensure the desired H ∞ performance requirements, and the explicit form of the estimator gains can be obtained by solving some matrix inequalities. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed state estimation approach. [ABSTRACT FROM AUTHOR]

This paper aims at the problem on state estimation of complex-valued neural networks with two additive time-varying delays. Via selecting appropriate Lyapunov–Krasovskii functionals and utilizing reciprocally convex approach and applying matrix inequality technique to analysis, a delay-dependent sufficient condition is derived in the form of linear matrix inequalities (LMIs) to estimate the neuron state with some observed output measurements so as to guarantee the global asymptotic stability of the error-state system. A numerical example is provided to illustrate the feasibility of the obtained result. [ABSTRACT FROM AUTHOR]

In this paper, the problem on synchronization is investigated for a class of impulsive complex-valued neural networks with discrete and distributed time-varying delays as well as leakage delay. By constructing appropriate Lyapunov–Krasovskii functional, and using Newton–Leibniz formulation, inequality technique and free-weighting matrix method, several sufficient criteria to guarantee the global μ -synchronization are derived for the considered impulsive complex-valued neural networks. The provided conditions are expressed in terms of linear matrix inequalities, and are dependent on the sizes of discrete delays, distributed delays and leakage delay. An example with simulations is provided to verify the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

In this paper, the problem of global µ -stability for quaternion-valued neural networks with time-varying delays and unbounded distributed delays is investigated. To avoid the non-commutativity of quaternion multiplication, the quaternion-valued neural networks is decomposed into two complex-valued systems. By employing the homomorphic mapping principle, a sufficient condition for the existence and uniqueness of equilibrium point of the considered quaternion-valued neural networks is proposed in the form of linear matrix inequality (LMI) in complex-valued domain. Further, the appropriate Lyapunov–Krasovkii functional is constructed in the Hermitian quadratic form, and sufficient condition to ensure the global µ -stability of the equilibrium point is obtained by using inequality technique. Finally, two numerical examples with simulations are provided to verify the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

In this paper, the problem of finite-horizon H ∞ fault estimation is investigated for a class of networked time-varying stochastic systems with randomly occurring nonlinearities and state- and disturbance-dependent noises (also called ( x, v )-dependent noises). An event-triggered scheme is proposed to reduce data transmission burden where the current measurement is transmitted only when the certain condition is satisfied. The aim of the addressed problem is to design a fault estimator, in the presence of randomly occurring nonlinearities and ( x, v )-dependent noises, such that faults can be estimated through measurement outputs. By employing the stochastic analysis method, the sufficient conditions are derived to guarantee that the error dynamics of estimations satisfies a prescribed H ∞ performance constraint. Moreover, the parameters of fault estimator can be calculated via the recursive linear matrix inequality (RLMI) approach. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the l 2 − l ∞ state estimation problem for discrete-time switched neural networks with time-varying delay. The main objective is to design a mode-dependent state estimator such that the error dynamics is exponentially stable with a weighted l 2 − l ∞ performance level. By incorporating the novel l 2 − l ∞ performance analysis approach, the augmented piecewise Lyapunov-like functionals, the discrete Wirtinger-based inequality and the average-dwell-time switching, less conservative sufficient conditions are proposed by means of linear matrix inequalities. A numerical example is given to illustrate the effectiveness and benefits of the obtained results. [ABSTRACT FROM AUTHOR]

In this paper, the particle filtering problem is investigated for a class of networked nonlinear systems with random one-step sensor delay and missing measurements. The phenomena of missing measurements and random one-step sensor delay are modeled by two random variables, both obeying the Bernoulli distribution. Here, we derive an explicit expression for the likelihood function when the possible occurrence of one-step sensor delay and measurement loss is taken into consideration. Based on this likelihood function, we propose a novel particle filtering algorithm to treat the nonlinear estimation problem in the simultaneous presence of random sensor delay and measurement loss. Finally, a simulation example is given to illustrate the feasibility and advantages of the proposed filtering scheme compared with traditional particle filtering algorithm. [ABSTRACT FROM AUTHOR]

This paper discusses the distributed optimization problem for the continuous-time and discrete-time multi-agent systems. For such a problem, each agent possesses a local convex cost function only known by itself and all the agents converge to the optimizer of the sum of the local cost function through estimating the optimal states of the local cost function and exchanging states information between agents. Sufficient conditions for convergence to the optimizer of the continuous-time and discrete-time algorithms are provided by making use of the Lyapunov method. We also obtain the least convergence rate for the modified algorithm. Moreover, numerical simulations are supplied to testify the results we present. [ABSTRACT FROM AUTHOR]

In this paper, the dynamics including boundedness and stability for a general class of complex-valued neural networks with variable coefficients and proportional delays are investigated. By employing inequality techniques and mathematical analysis method, some sufficient criteria to guarantee boundedness and global exponential stability are established for the considered neural networks. As a special case that the coefficients of networks are constants, sufficient criteria are also derived to guarantee the existence, uniqueness and global exponential stability of the equilibrium point. This work generalizes and improves previously known results, and the obtained criteria can be tested and applied easily in practice. An illustrative example demonstrates the feasibility of the proposed results. [ABSTRACT FROM AUTHOR]

This paper investigates the L 2 – L ∞ state estimation problem for a class of delayed neural networks. Attention is focused on the design of a full-order state estimator such that the prescribed L 2 – L ∞ performance constraint can be ensured. By utilizing the time-delay information sufficiently, a novel L 2 – L ∞ performance analysis approach is proposed in this paper for the first time. Based on such an approach, the less conservative sufficient conditions are established in terms of linear matrix inequalities under which the L 2 – L ∞ performance level can be achieved for the estimation error dynamics. Several numerical examples show that the proposed approach in this paper is explicitly effective in reducing the possible conservatism. [ABSTRACT FROM AUTHOR]

In this paper, the resilient state estimation problem is investigated for a class of discrete recurrent neural networks (RNNs) subject to mixed time-delays, missing measurements and stochastic disturbance. The mixed time-delays consist of randomly occurring time-delay and distributed sensor delays, where a random variable obeying the Bernoulli distribution is employed to characterize the phenomenon of randomly occurring time-delay. In addition, the phenomena of the multiple missing measurements are characterized by introducing a set of mutually independent random variables, which reflect that each sensor could have individual missing probability. Meanwhile, the additive variation of the estimator gain is considered to reflect the possible parameter deviations when implementing the state estimation algorithm. Our main purpose is to design a resilient state estimator such that, in the presence of multiple missing measurements, randomly occurring time-delay and distributed sensor delays, the estimation error dynamics is exponentially stable in the mean square. A sufficient condition is established to guarantee the existence of the resilient state estimator and the explicit expression of the desired estimator gain is given based on the solutions to some matrix inequalities. Finally, we use a numerical example to verify the validity of the presented resilient state estimation method. [ABSTRACT FROM AUTHOR]

The sampled-data cluster formation is proposed and considered for nonlinear multi-agent systems. The cluster formation problem, which is try to make all the agents divided into separated groups and each of the group realize distinct complement formation. Formation control protocol is constructed based on the event-triggered sampled-data method. Since the event condition for each agent is designed by using its own sampling information and latest transmitted information of its neighbors, the event condition can be intermittently examined at constant sampling instants. A sufficient condition is given to solve the cluster formation problem for fixed and switching topologies. Finally, the effectiveness of the control mechanism is demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, by applying the exponential dichotomy theory on time scales, Banach contraction mapping principle and differential inequality analysis techniques, we establish some sufficient conditions for the existence and global exponential stability of almost automorphic solution for neutral type high-order Hopfield bidirectional associative memory (BAM) neural networks with time-varying leakage delays on time scales. In the addressed system, we not only consider the effects of the first-order neutral terms on networks, but also investigate the influences of the second-order neutral terms on the networks. High-order Hopfield BAM neural networks with continuously distributed leakage delays on time scales are also considered. Our results are completely new even if time scale T = R or T = Z and complementary to the previously existing results. Finally, three examples with numerical simulations are presented to illustrate the feasibility of our proposed theoretical results. [ABSTRACT FROM AUTHOR]

This paper focuses on presenting a new memristor model with a general window function. Three adjustable parameters are introduced such that it is more flexible. It is seen that several well-known window functions are the special cases of the general window function. Some issues (e.g., the boundary lock and limited scalability) encountered in previously reported memristor models are thus efficiently resolved. Furthermore, fine-resolution programmable resistance is achieved by controlling the parameters of the general memristor model. The programmable analog filter and gain amplifier are finally implemented to illustrate the applications of the developed memristor model. [ABSTRACT FROM AUTHOR]

This paper investigates the design and analysis problem of H ∞ filter for a class of nonlinear systems based on T-S fuzzy models with both multiplicative noises and redundant channels, which are governed by a set of Bernoulli distributed white sequences. The packet dropout’s probability of the i -th channel depends on random Bernoulli variables. The aim of this study is to design and analyze an H ∞ filter that can stabilize the T-S fuzzy filtering error dynamics. By utilizing both Lyapunov functional approach and stochastic analysis technique, we establish some sufficient conditions such that the addressed system is asymptotically stable in the mean square with a given H ∞ performance. The needed filtering parameters are obtained by making use of the matrix inequalities’ solution. In the end, an example is given to demonstrate the effectiveness and usefulness of the proposed filtering approach. [ABSTRACT FROM AUTHOR]

In this paper, we study the set stabilization of Boolean networks (BNs) under pinning control strategy. First, the algebraic expression of BN is obtained by using semi-tensor product of matrices. Based on the algebraic expression, we give a method to choose pinning nodes, and achieve set stabilization by controlling these selected nodes. A matrix is further defined to design state feedback controllers. Based on the matrix set, state feedback controllers can be obtained quickly and the computational complexity can be reduced. Finally, an example is given to illustrate the design procedure of pinning controllers. [ABSTRACT FROM AUTHOR]

In the paper, the quaternion-valued neural networks (QVNNs) with non-differentiable time-varying delays are considered. Firstly, by using the method of plural decomposition, we decompose the QVNNs into two complex-valued neural networks. Some sufficient criteria in linear matrix inequality (LMI) form are derived to guarantee the existence and uniqueness of the equilibrium point for considered QVNNs by using the homeomorphism mapping principle of complex domain. Secondly, based on applying the free weighting matrix method and constructing appropriate Lyapunov–Krasovskii functional, several conditions are established in LMIs to ensure the the global μ − stability of QVNNs. Finally, by employing the predictor-corrector approach, two numerical examples are provided to show the feasibility and availability of the obtained result. [ABSTRACT FROM AUTHOR]

This paper proposes a niching evolutionary algorithm with adaptive negative correlation learning, denoted as NEA_ANCL, for training the neural network ensemble. In the proposed NEA_ANCL, an adaptive negative correlation learning, in which the penalty coefficient λ is set to dynamically change during training, has been developed. The adaptation strategy is based on a novel population diversity measure with the purpose of appropriately controlling the trade-off between the diversity and accuracy in the ensemble. Further, a modified dynamical fitness sharing method is applied to preserve the diversity of population during training. The proposed NEA_ANCL has been evaluated on a number of benchmark problems and compared with related ensemble learning algorithms. The results show that our method can be used to design a satisfactory NN ensemble and outperform related works. [ABSTRACT FROM AUTHOR]

Finite-time stability of a class of fractional-order complex-valued memristor-based neural networks with both leakage and time-varying delays is investigated in this paper. By employing the set-valued map and differential inclusions, the solutions of memristor-based systems are intended in Filippov’s sense. Via using Hölder inequality, Gronwall–Bellman inequality and inequality scaling skills, sufficient conditions to guarantee the stability of the system are derived when 0 < α < 1 2 and 1 2 ≤ α ≤ 1 , respectively. Finally, two numerical examples are designed to illustrate the validity and feasibility of the obtained results. [ABSTRACT FROM AUTHOR]

This paper discusses the stability in Lagrange sense for complex-valued neural networks with time-varying discrete delays and distributed delays as well as leakage delay. By constructing an appropriate Lyapunov–Krasovskii functional, and employing free-weighting-matrix approach and inequality techniques in matrix form, a sufficient criterion to guarantee global exponential stability in Lagrange sense is obtained for the investigated neural networks. The given criterion is delay-dependent and is shown as linear matrix inequalities in complex domain, which can be calculated numerically applying valid YALMIP toolbox in MATLAB. A numerical example is provided to manifest the validity of the proposed result. [ABSTRACT FROM AUTHOR]

In this paper, global asymptotic stability of impulsive fractional-order complex-valued neural networks with time delay is considered. By employing contraction mapping principle, comparison theorem and inequality scaling skills, several sufficient conditions to ensure the existence, uniqueness and global asymptotic stability of the equilibrium point for the considered neural networks are established. A numerical example illustrates the validity and feasibility of the obtained result. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid learning approach, which combines the extreme learning machine (ELM) with a new switching delayed PSO (SDPSO) algorithm, is proposed for the problem of the short-term load forecasting (STLF). In particular, the input weights and biases of ELM are optimized by a new developed SDPSO algorithm, where the delayed information of locally best particle and globally best particle are exploited to update the velocity of particle. By testing the proposed SDPSO-ELM in a comprehensive manner on a tanh function, this approach obtain better generalization performance and can also avoid adding unnecessary hidden nodes and overtraining problems. Moreover, it has shown outstanding performance than other state-of-the-art ELMs. Finally, the proposed SDPSO-ELM algorithm is successfully applied to the STLF of power system. Experiment results demonstrate that the proposed learning algorithm can get better forecasting results in comparison with the radial basis function neural network (RBFNN) algorithm. [ABSTRACT FROM AUTHOR]