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This paper focuses on studying observer-based controller synthesis for nonlinear plant subject to parameter uncertainties, which is represented as interval type-2 T–S fuzzy model. An interval type-2 fuzzy augmented system is formed by an interval type-2 T–S fuzzy model, an interval type-2 fuzzy observer and an interval type-2 fuzzy controller connected in a closed loop. Based on the Lyapunov stability theory, the stability of the interval type-2 fuzzy augmented systems is investigated. Some matrix inequality techniques are applied to derive stability conditions in the form of linear matrix inequalities (LMIs). With the consideration of the information of lower and upper membership functions, more relaxed stability conditions are obtained to guarantee the system stability. Finally, two numerical example are provided to illustrate the effectiveness of the proposed conditions. [ABSTRACT FROM AUTHOR]

In this paper, we investigate finite-time H ∞ control of uncertain switched neural networks of neutral type with distributed time varying delays. The mathematical model of the switched neural networks with distributed delays is established in which a set of neural networks are used as individual subsystems and an arbitrary switching rule is assumed, stability analysis for such switched neural networks is addressed based on the linear matrix inequality (LMI) and finite-time bounded average dwell time approach. Finite-time H ∞ performance analysis was established for switched neural network of neutral type. Numerical examples are given to illustrate the usefulness of our proposed method. [ABSTRACT FROM AUTHOR]

In this paper the average consensus problem of multi-agent systems under the event-triggered controller is investigated. In event-triggered consensus, an event of each agent occurs only when the state measurement error exceeds a given threshold, and then the agent communicates with its neighbors and updates the control input at the event time. By appropriate event design, discontinuous threshold is employed and thus continuous communication among agents can be avoided to reduce energy consumption in practice. To avoid Zeno behavior, an event/time hybrid trigger is proposed to guarantee asymptotic consensus while excluding Zeno behaviors. Moreover, we develop a self-triggered algorithm to determine the event time instants to further avoid continuous self-state monitoring. Simulation results illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, the periodic solution of Cohen–Grossberg neural network with impulses is studied. Some new sufficient criteria are established to test the existence, exponential stability and stabilization of periodic solution. The results obtained depend mainly on the mean value of the integral of continuous periodic coefficients and the sum of impulsive jumps, and improve some previous related works. Three examples with numerical simulations are given to demonstrate the efficiency of proposed methods. [ABSTRACT FROM AUTHOR]

This paper investigates the path following control problem for an unmanned marine surface vessel (MSV) in the presence of uncertainties and input saturation. The backstepping technique augmented by a robust adaptive radial basis function neural network (RBFNN) and an auxiliary design system is employed as the main control framework. Based on the dynamic model of the MSV, an improved adaptive integral line-of-sight (LOS) guidance law is first proposed, which is suitable for any parametric paths and can deal with time-varying ocean currents. The guidance law calculates the desired yaw angle and estimates the currents. Then the controller is extended to cope with the MSV yaw tracking and velocity control by resorting to the augmented backstepping technique. The uncertainties of dynamics are compensated by the robust RBFNNs. Each robust RBFNN utilizes an n th-order smooth switching function to combine a conventional RBFNN with a robust control. The auxiliary design system is presented to analyze the effect of input saturation, and states of the auxiliary design system are used to develop the controller. This systematic design methodology is proved to achieve ultimate boundedness of the closed-loop MSV system. Simulations validate the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

Utilizing wavelet transform, in this paper an intelligent and optimum method is presented for islanding detection. The recommended relay is based on neuro-fuzzy system which is trained by using four different heuristic algorithms and finally among all of them, PSO with having the best results is elected for proposed relay. In this procedure, selection of mother wavelet and also proper input signals for islanding detection is done optimally. To do so, firstly, various islanding and switching in different loading conditions of network are simulated. Then, the eligible signals for islanding detection are sorted based on wavelet analysis and receiving operating characteristic (ROC) concept. Afterward, using four different algorithms, training of the designed relay is done by the sorted signals and the optimum condition of the fault detector is recognized. Reduction of non-detection zone approximately to zero, selection of optimum number of relay׳s input signal, fast detection, solving of threshold selection, appropriate signal selection and fast training via least information are the prominent features of the proposed method. Simulation results and comparison of them for different cases approve the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Consensus can be achieved under various topologies for multi-agent systems. Then, which one is the optimal? In this paper, we consider the problem of optimal topology for consensus of heterogeneous multi-agent systems. We assume that the system consists of a leader and several followers with heterogeneous dynamics. Firstly, we define a quadratic cost function composed of consensus error and control effort for the heterogeneous multi-agent system. Secondly, by using linear-quadratic regulator theory and matrix theory, we prove that the optimal position topology and the optimal velocity topology are star graphs. Finally, simulations are carried out to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Most existing dimensionality reduction algorithms have two disadvantages: their computational cost is high and they cannot estimate the intrinsic dimension of the original dataset by themselves. To deal with these problems, in this paper we propose a fast linear dimensionality reduction method named Orthogonal Component Analysis (OCA). While avoiding solving eigenproblem and matrix inverse problem, OCA successfully achieves high-speed orthogonal component extraction. By proposing an adaptive threshold scheme, OCA is able to estimate the dimension of the feature space automatically. Meanwhile, the algorithm is guaranteed to be numerical stable. In the experiments, OCA is compared with several typical dimensionality reduction algorithms. The experimental results demonstrate that as a universal algorithm, OCA is efficient and effective. [ABSTRACT FROM AUTHOR]

In this paper, a global finite-time heading control (GFHC) scheme for surface vehicles is proposed. The salient features of the GFHC scheme are triple-fold: (1) A discontinuous control law is proposed to guarantee the finite-time stability of the entire closed-loop heading control system. (2) The finite-time convergence leads to accurate heading control and remarkable disturbance rejection. (3) Furthermore, it reveals that the proposed GFHC scheme treats asymptotic heading controllers as special cases. Simulation studies and comprehensive comparisons on various scenarios demonstrate the effectiveness and superiority of the proposed GFHC scheme. [ABSTRACT FROM AUTHOR]

In current decades, various Evolutionary Algorithms(EAs) raise as well as many kinds of benchmarks are popular in evaluations of EAs׳ performances. Since there exists randomness in EAs׳ performances, the evaluations are made by a large number of runs in simulations or experiments in order to present a relatively fair comparison. However, there still exit several problems that have not been well explained. Does it make sense to deem two algorithms have equal ability if they have same final results? Is it convinced to decide winners or losers in comparisons just by tiny difference in performances? Besides the final results, how to compare algorithms׳ performances during the optimization iterations? In this paper, a neural network classifier based on extreme learning machine (ELM) is proposed to solve these problems. A novel role of classifier is first proposed to convince the differences between algorithms. If the classifier succeeds to classify algorithms based on their performances recorded in all generations, we deem the two algorithms have so convinced difference that comparisons of two algorithms can reflect algorithms׳ disparity. Therefore, the conclusions to judge the two algorithms are feasible and acceptable. Otherwise, if classifiers cannot distinguish two algorithms, we deem the two have similar performances so that it is meaningless to differ two algorithms just by tiny differences. By employing a set of classical benchmarks and six EAs, the simulations and computations are conducted. According to the analysis results, the proposed classifier can provide more information to reflect true abilities of algorithms, which is a novel view to compare EAs. [ABSTRACT FROM AUTHOR]

In this paper, we propose two alternative schemes of fast online sequential extreme learning machine (ELM) for training the single hidden-layer feedforward neural networks (SLFN), termed as Cholesky factorization based online regularized ELM with forgetting mechanism (CF-FORELM) and Cholesky factorization based online kernelized ELM with forgetting mechanism (CF-FOKELM). First, the solutions of regularized ELM (RELM) and kernelized ELM (KELM) using the matrix Cholesky factorization are introduced; then the recursive method for calculating Cholesky factor of involved matrix in RELM and KELM is designed when RELM and KELM are applied to train SLFN online; consequently, the CF-FORELM and CF-FOKELM are obtained. The numerical simulation results show CF-FORELM demands less computational burden than Dynamic Regression ELM (DR-ELM), and CF-FOKELM also owns higher computational efficiency than both FOKELM and online sequential ELM with kernels (OS-ELMK), and CF-FORELM is less sensitive to model parameters than CF-FOKELM. [ABSTRACT FROM AUTHOR]

This paper studies synchronization problems of complex dynamical network with mixed coupling via impulsive control. The model involves both advanced and delayed arguments, and the problem of the existence of the model is analyzed. Based on the stability theory for impulsive differential equations, an effective impulsive control scheme is proposed to achieve synchronization for complex dynamical networks. Finally, an example with numerical simulations is given to illustrate our results. [ABSTRACT FROM AUTHOR]

This paper explores the complete synchronization of chaotic neural networks with time delays by intermittent control with two switches in a control period. Some sufficient conditions for complete synchronization are derived by using Lyapunov stability theory. Numerical simulations are also presented to show their good agreements with the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper focuses on the hybrid effects of coupling, time-varying delay, intermittent controller on memristor-based neural networks. Firstly, we constructed an array of linearly coupled memristor-based neural networks with time-varying delay. Secondly, our sufficient conditions, ensuring the stability and synchronization, are dependent on coupling and intermittent control and show coupling and intermittent control on the stability and synchronization of memristor-based neural networks. Finally, numerical simulations were given to verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates the projective synchronization between two delayed networks of different sizes with nonidentical nodes and unknown parameters. The two complex networks are of diverse dynamical nodes, different topological structures and different sizes. Both networks contain unknown parameters and coupling delay. Based on stability theory, the bidirectional coupling control condition is proposed to ensure the realization of projective synchronization between the two delayed networks. During synchronization, the unknown parameters are successful estimated by adaptive updated laws. Furthermore, in numerical simulations, the Small-World network and Scale-Free network based on well-known Lorenz system and Chen system are constructed. The results verified the effectiveness of the proposed synchronization method. [ABSTRACT FROM AUTHOR]

This paper concerns with the anti-periodic solutions for a class of shunting inhibitory cellular neural networks model with oscillating coefficients in leakage terms. By applying contraction mapping fixed point theorem and differential inequality techniques, we establish some sufficient conditions for the existence and exponential stability of anti-periodic solutions for the model, which complement with some recent ones. Moreover, an example and its numerical simulation are given to support the theoretical results. [ABSTRACT FROM AUTHOR]

This paper focuses on studying the H ∞ state estimation of static neural networks with interval time-varying delays via augmented Lyapunov–Krasovskii functional. By constructing a suitable augmented Lyapunov–Krasovskii functional with triple integral terms and linear matrix inequality technique, the delay-dependent criteria are conferred so that the error system is globally asymptotically stable with H ∞ performance. The activation functions are assumed to satisfy sector-like nonlinearities. The desired estimator gain matrix can be characterized in terms of the solution to linear matrix inequalities, which can be easily solved by some standard numerical algorithms. Numerical simulation is given to demonstrate the effectiveness and superiority of the proposed method comparing with some existing results. [ABSTRACT FROM AUTHOR]

This paper studies the coordination of nonlinear multi-agent systems with multiple leaders and stochastic disturbance. Two impulsive control algorithms are proposed to make all agents to track the convex set determined by the multiple leaders under both fixed and switching topologies. Based on Lyapunov function and comparison principle of impulsive systems, some results on the convergence of the controlled systems are obtained. Finally, some numerical simulations demonstrate the validity of the proposed control algorithms. [ABSTRACT FROM AUTHOR]

The paper presents a new automatic architecture for the control of aircraft lateral-directional motion during landing; the system controls the lateral angular deviation of aircraft longitudinal axis with respect to the runway, by using a classical controller, a radio-navigation system, a system for the calculation of the distances between aircraft and the runway radio-markers, and an adaptive controller mainly used for the control of aircraft roll angle and its deviation with respect to the runway. The adaptive control system uses the dynamic inversion concept, a dynamic compensator, a neural network trained by the system׳s estimated error vector (signal provided by a linear observer), and a Pseudo Control Hedging block. The new designed adaptive architecture is software implemented and validated by complex numerical simulations; the obtained characteristics are very good and prove the new architecture׳s stability and its small overshoots. [ABSTRACT FROM AUTHOR]

In this paper, the consensus problem of multi-agent nonlinear systems with control gain error is studied. Based on the theory of impulsive differential equations, Lyapunov stability theory and algebraic graph theory, some impulsive consensus conditions are given to realize the consensus of a class of multi-agent nonlinear systems. Compared with the existing investigations of impulsive consensus of multi-agent systems, the proposed impulsive control protocol with control gain error is more rigorous and effective in practical systems. Two numerical simulations are verified to confirm the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Background modeling and subtraction, the task to detect moving objects in a scene, is an important step in video analysis. In this paper, we present a novel moving object detection method based on Online Low Rank Matrix Recovery and graph cut from monocular video sequences. First, use the K-SVD method to initialize the dictionary to construct the background model, perform foreground detection with augmented Lagrange multipliers (ALM) and refine foreground values by spatial smooth constraint to extract the background and foreground information; Then obtain the clusters of foreground and background respectively using mean shift clustering on the background and foreground information; Third, initialize the S/T Network with corresponding image pixels as nodes (except S/T node); Calculate the data and smoothness term of graph; Finally, use max flow/minimum cut to segmentation S/T network to extract the motion objects. Online dictionary learning is adopted to update the background model. Experimental results on indoor and outdoor videos demonstrate the efficiency of our proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a class of delayed Cohen–Grossberg neural networks without intra-neuron delay is investigated. Firstly, a modified delayed Cohen–Grossberg neural network is introduced, in which communication delays between different neurons are considered and the intra-neuron delays are negligible and ignored. Furthermore, by utilizing homeomorphism theory, matrix theory, Lyapunov functional method and some analysis techniques including classified discussion, reduction ad absurdum and component analysis, some new and less conservative criteria in terms of M -matrices are derived to ensure the existence, asymptotical stability and exponential stability of the equilibrium for the considered neural network. In our obtained results, the values of the self-connection strengths are reserved and play a central role, which improve and generalize some well-known corresponding results. Finally, some numerical simulations are given to demonstrate the effectiveness of the derived theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, based on recently developed deterministic learning (DL) theory, we investigate the problem of stabilization for an underactuated rigid spacecraft with unknown system dynamics. Our objective is to learn the unknown underactuated system dynamics while tracking to a desired orbit and design the control law to achieve stabilization. First, the system dynamic and kinematic equations are given, the kinematic equation is described by the ( w , z ) parametrization. Second, an adaptive neural network (NN) controller with the employed radial basis function (RBF) is designed to guarantee the stability of the underactuated rigid spacecraft system and the tracking performance. The unknown dynamics of underactuated rigid spacecraft system can be approximated by NN in a local region and the learned knowledge is stored in constant RBF networks. The accessorial variables γ 1 and γ 2 are imported in the designing course of the control laws via backstepping method. Third, when repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability with little effort. Finally, simulation studies are included to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper devotes to stability analysis of continuous time and discrete time bidirectional associative memory (BAM) neural networks whose parameters are randomly varying in a finite state Markov chain sense. Based on the ergodic theory of continuous time Markov chain, the matrix measure approach and Lyapunov theory, almost sure stability and exponential stability in the mean square for continuous time BAM neural networks are derived. We also present some new stability results for discrete time BAM neural networks with the help of the law of large numbers. Meanwhile, some examples with numerical simulations are given to show that the Markov chain plays an important role in stability of neural networks. [ABSTRACT FROM AUTHOR]

This paper considers the convergence problem of shunting inhibitory cellular neural networks with delays and oscillating coefficients in leakage terms. Based on a new integral inequality technique, some sufficient conditions are derived to ensure that all solutions of the addressed system converge exponentially to zero vector. Also, two examples and their numerical simulations are given to illustrate the main results. [ABSTRACT FROM AUTHOR]

Multiple coordination corresponds to a diversified synchronous regime where different subgroups in one multi-agent system may emerge several distinct collective behaviors respectively. This paper is devoted to propose and analyze hybrid subgroup coordination of multi-agent systems under generic directed topologies. Due to unanticipated practical situations, agents in one subgroup are supposed to achieve a synchronous scenario asymptotically, such as average of agents’ initial states or its bounded region, while there is no agreement between any two distinct subgroups. Certain assumptions are imposed on the underlying topology for agents to evolve into several subgroups. Explicit distributed control algorithms are designed by adopting nonidentical extra-subgroup and intra-subgroup information exchange, and detailed convergence analysis is presented based on techniques of graph and matrix. Relevant simulation work is further carried out to illuminate the developed theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the mode-dependent projective synchronization problem of stochastic neural networks with mixed time-delays is investigated. By considering a proper sliding model surface, a sliding mode controller (SMC) is synthesized to guarantee the reach-ability of the specified sliding surface. The globally asymptotically stable of the sliding mode dynamical system is surveyed with the Lyapunov stability theory and linear matrix inequality (LMI) technique. A sufficient projective synchronization criterion for the stochastic neural networks is derived. Finally, numerical simulations are exploited to illustrate the effectiveness of the main results. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel technique termed miSFM for the simulation of crowd evacuation. miSFM take merits of both Mutual Information (MI) and Social Force Model (SFM). More specifically, MI of interacting agents is adopted to determine the level of order within a crowd during an evacuation. In such a way, SFM can be improved by adapting the forces involved at microscopic level between mutually interacting agents. The key innovation lies in highlighting how the dynamic adjustment of SFM parameters reveals much more realistic crowd movements for the evacuation simulation. Extensive experiments over several alternative and state-of-the-art works demonstrate the advantages of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we discussed non-fragile state estimation problem for a class of memristive neural networks with two different types of memductance functions and uncertain time-varying delays. The required results are derived by using a suitable Lyapunov–Krasovskii functional (LKF) and using linear matrix inequality (LMI) approach together with Wirtinger-type inequality analysis. The sufficient conditions are presented for the existence of non-fragile state estimator based on the combined H ∞ and passivity performance criterions. The results are proposed in terms of LMIs, which can guarantee the global asymptotic stability of the error dynamics between the considered memristive RNNs and its non-fragile observer. Finally, a numerical example is presented to illustrate the effectiveness of the theoretical results via simulations. [ABSTRACT FROM AUTHOR]

Flexible loads are important resources to help maintain the power balance of smart grid. However, the control center is not able to exactly sense and control them because of their large quantity and wide distribution. In order to get the flexible loads to participate in the dynamic power system look-ahead scheduling, this paper proposes a three-layer ‘centralized coordination, distributed control’ structure in which the load agents are introduced to perform the coordination based on the consensus control. Then, the optimal control strategy of the control center and the consensus control strategy of the load agents are designed. With the communication among different flexible loads, the distributed cooperative control is implemented. At last, the efficiency of the proposed mode and strategies is proven through the simulation carried out under the standard IEEE 9-bus system, and the effect of topology and communication is also analyzed. [ABSTRACT FROM AUTHOR]

This paper discusses a new policy iteration Q -learning algorithm to solve the infinite horizon optimal tracking problems for a class of discrete-time nonlinear systems. The idea is to use an iterative adaptive dynamic programming (ADP) technique to construct the iterative tracking control law which makes the system state track the desired state trajectory and simultaneously minimizes the iterative Q function. Via system transformation, the optimal tracking problem is transformed into an optimal regulation problem. The policy iteration Q -learning algorithm is then developed to obtain the optimal control law for the regulation system. Initialized by an arbitrary admissible control law, the convergence property is analyzed. It is shown that the iterative Q function is monotonically non-increasing and converges to the optimal Q function. It is proven that any of the iterative control laws can stabilize the transformed nonlinear system. Two neural networks are used to approximate the iterative Q function and compute the iterative control law, respectively, for facilitating the implementation of policy iteration Q -learning algorithm. Finally, two simulation examples are presented to illustrate the performance of the developed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates distributed containment control problems for second-order nonlinear multi-agent systems with dynamic leaders. In the presence of bounded unknown nonlinearity, a distributed control algorithm based on High-frequency Feedback Robust Control is proposed. Under this algorithm, a sufficient condition is proposed to ensure that all the followers will converge to the dynamic convex hull formed by dynamic leaders and the containment error can be ultimately bounded for the directed graph where at least one leader has a directed path to each follower and the communication among the followers are undirected. Numerical simulations are also presented to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

A hierarchical network composed of two interacting rings each of which consists of n identical cells with an unidirectional coupling is the topic of this paper. We present a detailed discussion about the linear stability of the equilibrium by analyzing the associated characteristic equation. The local Hopf bifurcation and spatio-temporal patterns of bifurcating periodic oscillations are also given by employing the symmetric Hopf bifurcation theory for delay differential equations. In particular, by using the normal form theory and the center manifold theorem, we derive the formula determining the direction of the Hopf bifurcation and the stability of the bifurcated periodic orbits. An example with numerical simulations is presented to illustrate our theoretical results. [ABSTRACT FROM AUTHOR]

In this paper we propose the gravitational search algorithm to design PID control structures. The controller design is performed considering the objectives of set-point tracking and disturbance rejection, minimizing the integral of the absolute error criterion. A two-degrees-of-freedom control configuration with a feedforward pre-filter inserted outside the PID feedback loop is used to improve system performance for both design criteria. The pre-filter used is a Posicast controller designed simultaneously with a PID controller. Simulation results are presented which show the proposed technique merit. [ABSTRACT FROM AUTHOR]

This paper proposes and investigates a finite-time convergent neural dynamics (FTCND) for online solution of time-varying linear complex matrix equation in complex domain. Different from the conventional gradient-based neural dynamical method, the proposed method utilizes adequate time-derivative information of time-varying complex matrix coefficients. It is theoretically proved that our FTCND model can converge to the theoretical solution of time-varying linear complex matrix equation within finite time. In addition, the upper bound of the convergence time is derived analytically via Lyapunov theory. For comparative purposes, the conventional gradient-based neural dynamics (GND) is developed and exploited for solving such a time-varying complex problem. Computer-simulation results verify the effectiveness and superiorness of the FTCND model for solving time-varying linear complex matrix equation in complex domain, as compared with the GND model. [ABSTRACT FROM AUTHOR]

This paper discusses the stability condition for discrete-time multi-valued recurrent neural networks (MVNRNNs) in asynchronous update mode. In the existing research literature, an MVNRNN in asynchronous update mode has been found convergent if its weight matrix is Hermitian with nonnegative diagonal entries. However, our finding has been that the weight matrix with zero diagonal entries cannot guarantee the network stability. Furthermore, the new stability condition and proof is offered to allow diagonal entries to be complex-valued, which extends previous theoretical result. Simulation results are used to illustrate the theory. [ABSTRACT FROM AUTHOR]

In this paper, we establish a neural-network-based decentralized control law to stabilize a class of continuous-time nonlinear interconnected large-scale systems using an online model-free integral policy iteration (PI) algorithm. The model-free PI approach can solve the decentralized control problem for the interconnected system which has unknown dynamics. The stabilizing decentralized control law is derived based on the optimal control policies of the isolated subsystems. The online model-free integral PI algorithm is developed to solve the optimal control problems for the isolated subsystems with unknown system dynamics. We use the actor-critic technique based on the neural network and the least squares implementation method to obtain the optimal control policies. Two simulation examples are given to verify the applicability of the decentralized control law. [ABSTRACT FROM AUTHOR]

This paper addresses the robust consensus problem of fractional-order linear multi-agent systems with positive real uncertainty where the fractional-order α satisfies 0 < α < 2 . For multi-agent systems with fixed undirected topology, a distributed protocol is proposed based on the information of second-order neighbors. Then, new LMI-based sufficient conditions for robust consensus of fractional-order multi-agent systems are obtained and the feedback matrix is also determined accordingly. Finally, numerical simulation examples are presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper attempts to probe into the calcium oscillation behavior in a biological mathematical model, from which the conditions of complete synchronization are derived based on gap-junction. And thus the ranges of coupling strength g are obtained under the ring coupling and the global one for identical cells. It is observed that bigger synchronization threshold is needed for the ring coupling than the global one. In particular, we point out that the unidentical cells require stronger coupling to make the system reach nearly complete synchronization by means of numeric simulation. [ABSTRACT FROM AUTHOR]

This paper studies a class of complex-valued neural networks with time-varying delays. By using the conjugate system of the complex-valued neural networks and Brouwer׳s fixed point theorem, sufficient conditions to guarantee the existence and uniqueness of an equilibrium are obtained. Some criteria on globally exponential stability of the equilibrium of the complex-valued neural networks are also established by using a delay differential inequality. These results are easy to apply to the study of the complex-valued neural networks whether their activation functions are explicitly expressed by separating their real and imaginary parts or not. Two examples with numerical simulations are given to highlight the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

This paper considers the cooperative robust output regulation for second-order discrete-time multi-agent systems. Each agent could access the information with its neighbors under the directed network topology. A distributed dynamic state feedback control law is designed. A stability region is given for the discrete-time multi-agent systems, and internal model method is used for the robust output regulation problem. At last, numerical simulation is utilized to show the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

In this paper we consider a recurrent neural network model consisting of two neurons and analyze its stability using the associated characteristic model. In order to analyze the multiple quasi-periodic orbits, the strong resonance of this system, in particular that known as the R 2 bifurcation, is also studied. In the case of two neurons, one necessary condition that yields the bifurcation is found. In addition, the direction of the R 2 bifurcation is determined by applying normal form theory and the center manifold theorem. The simple conditions for ensuring the existence of multiple quasi-periodic orbits are given. The strong resonance phenomenon is analyzed using numerical simulations and is related with the codimension-two bifurcation of the high-iteration map. [ABSTRACT FROM AUTHOR]

In order to improve the precision and diversity of individual neural networks to improve the performance of neural network ensemble (NNE), this paper proposes a method of generating individual neural networks for ensembling based on twice clustering. By varying the network training data, enabling the samples to reflect the real data distribution, increasing the diversity between the training data to increase the diversity between the networks, thus improving the performance of NNE. First, using k -fold cross validation method to divide the original dataset. k -Fold cross validation, which learning samples from multiple directions, can effectively avoid falling into local minimum value. Because both the training and validation samples are as far as possible to participate in the learning, one can get satisfactory effect of learning. Secondly, choosing all the training samples to cluster for the first time to form once clustering subclasses, and then performing the twice clustering for each subclass to form the sample subsets of each subclass. Through affinity propagation (AP) clustering makes the otherness criterion of “similar in classes, different between classes” maximize, the samples in the class can reflect the real data distribution. Finally, according to the permutation and combination, selecting a subset from each twice clustering of each subclass to construct a training set. So the individual neural networks are generated with the bigger diversity, the smaller size of the training data and the training data can also reflect the real data distribution, and the ensemble of these individual neural networks can get better performance. Simulation experiments on nine datasets show that our proposed method here is effective. [ABSTRACT FROM AUTHOR]

This paper investigates the hybrid synchronization problem of two coupled complex networks with fractional-order dynamical nodes. Based on the fractional-order Lyapunov stability theorem, some sufficient conditions are derived to realize the hybrid synchronization of two coupled fractional-order networks. Under suitable condition, two coupled networks can reach the hybrid synchronization, i.e., the outer synchronization between the drive and response networks, and the inner synchronization in each networks. Numerical simulations demonstrate the effectiveness and feasility of the proposed synchronization protocol. [ABSTRACT FROM AUTHOR]

The prediction of vessel traffic flow is complicated, its accuracy is influenced by uncertain socio-economic factors, especially by the singular points existed in the statistical data. Recently, the robust v -support vector regression model ( R SVR) has been successfully employed to solve non-linear regression and time-series problems with the singular points. This paper will firstly propose a novel hybrid algorithm, namely chaotic cloud simulated annealing genetic algorithm (C cat CSAGA) for optimizing the parameters of R SVR, to improve the performance of vessel traffic flow prediction. In which, the proposed C cat CSAGA employs cat mapping to carefully expand variable searching space, to overcome premature local optimum, and uses cloud model efficiently to search a better solution in a small neighborhood of the current optimal solution, to improve the search efficiency. Secondly, the kernel principal component analysis (KPCA) algorithm is adopted to determine the final input vectors from the candidate input variables. Finally, a numerical example of vessel traffic flow and its influence factors data from Tianjin are employed to test the forecasting performance of the proposed K R SVR-C cat CSAGA model. [ABSTRACT FROM AUTHOR]

In this paper, a class of simple n -dimensional neutral Hopfield-type neural networks is considered. Based on a class of nonlinear algebraic equations with n variables and some computational techniques in M -matrix, some sufficient conditions for global attractivity of the equilibrium of the n -dimensional neutral Hopfield-type neural networks are established. Numerical simulations are given to summarize the applications. [ABSTRACT FROM AUTHOR]

This paper considers a novel image compression technique called hybrid predictive wavelet coding. The new proposed technique combines the properties of predictive coding and discrete wavelet coding. In contrast to JPEG2000, the image data values are pre-processed using predictive coding to remove inter-pixel redundancy. The error values, which are the difference between the original and the predicted values, are discrete wavelet coding transformed. In this case, a nonlinear neural network predictor is utilised in the predictive coding system. The simulation results indicated that the proposed technique can achieve good compressed images at high decomposition levels in comparison to JPEG2000. [ABSTRACT FROM AUTHOR]

In this paper, we present a new model to measure the contour point distortion for the vertex-based shape coding with B-splines, called accurate distortion measurement using analytical model (ADMAM). Different from existing distortion measurements containing approximation, quantization or parameterization process, our distortion is defined on the original B-spline. It is modeled as the shortest distance of associated contour point from the original B-spline, which is in line with the subjective-based objective quality metric. The geometric relationships are introduced to simplify the model computation, followed by a hybrid admissible distortion checking algorithm to reduce the execution time. Our theoretical analysis and experimental results demonstrate that the ADMAM can lead to the smallest bit-rate among all the distortion measurements that guarantee the admissible distortion, when the operational rate-distortion optimal shape coding framework is applied. Moreover, if the original contour has N C points, it takes only O ( N C ) time for both peak and mean-squared segment distortion measuring paradigms, which is the lowest computational complexity among all the existing distortion measurements. [ABSTRACT FROM AUTHOR]

In this paper, the distributed node-to-node consensus problem is addressed for a class of linear multi-agent systems with time-varying pinning links by using relative state feedback. The coordination goal in the present work is to make the states of each follower track those of its corresponding leader asymptotically where only a small fraction of followers can sense the states of their corresponding leaders through some switched communication channels. By using tools from M -matrix theory and stability analysis of switched systems theory, it is theoretically shown that such a node-to-node consensus in the closed-loop multi-agent systems can be guaranteed if each follower can be directly or indirectly influenced by at least one leader over some uniformly bounded time intervals, with the inner coupling matrix as well as the coupling strength being appropriately designed. Distributed node-to-node consensus for multi-agent systems with general Lipschitz-type nonlinear dynamics is also investigated. Numerical simulations are finally performed to verify the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel Stereo-Vision-Assisted (SVA) model for depth map super-resolution. Given a low-resolution depth map as input, we recover a high-resolution depth map using the registered high-resolution color stereo image pair. First, based on the mutual benefits between raw depth map and features of high resolution color image, we model the relationship with two constraint terms of local and non-local priors and sufficiently explore their complementary nature. Moreover, by considering reliable disparity pixels calculated from stereo matching algorithm, we formulate a stereo disparity regularization term to further reinforce the preservation of fine depth detail. Hence, our SVA objective function includes a non-local prior constraint, a local prior constraint and a stereo disparity prior constraint. In addition, we employ an efficient algorithm to optimize the objective function. Experimental results demonstrate that our approach can obtain superior performance in comparison with other methods. [ABSTRACT FROM AUTHOR]