ADAPTIVE PINNING SYNCHRONIZATION OF COUPLED NEURAL NETWORKS WITH MIXED DELAYS AND VECTOR-FORM STOCHASTIC PERTURBATIONS

In this article, we consider the global chaotic synchronization of general cou- pled neural networks, in which subsystems have both discrete and distributed delays. Stochastic perturbations between subsystems are also considered. On the basis of two sim- ple adaptive pinning feedback control schemes, Lyapunov functional method, and stochas- tic analysis approach, several sufficient conditions are developed to guarantee global syn- chronization of the coupled neural networks with two kinds of delay couplings, even if only partial states of the nodes are coupled. The outer-coupling matrices may be symmetric or asymmetric. Unlike existing results that an isolate node is introduced as the pinning target, we pin to help the network realizing synchronization without introducing any iso- late node when the network is not synchronized. As a by product, sufficient conditions under which the network realizes synchronization without control are derived. Numerical simulations confirm the effectiveness of the obtained results.

Dynamic Analysis of Some Impulsive Fractional-Order Neural Network with Mixed Delay

In this paper,the authors study some impulsive fractionalorder neural network with mixed delay. By the fractional integral and the definition of stability, the existence of solutions of the network is proved,and the sufficient conditions for stability of the system are presented. Some examples are given to illustrate the main results.

WEIGHTED PSEUDO ALMOST-PERIODIC SOLUTIONS OF SHUNTING INHIBITORY CELLULAR NEURAL NETWORKS WITH MIXED DELAYS

In this paper, we prove the existence and the global exponential stability of the unique weighted pseudo almost-periodic solution of shunting inhibitory cellular neural networks with mixed time-varying delays comprising different discrete and distributed time delays. Some sufficient conditions are given for the existence and the global exponential stability of the weighted pseudo almost-periodic solution by employing fixed point theorem and differential inequality techniques. The results of this paper complement the previously known ones. Finally, an illustrative example is given to demonstrate the effectiveness of our results.

Dynamical output feedback stabilization for neutral systems with mixed delays

This paper is concerned with the issue of stabilization for the linear neutral systems with mixed delays. The attention is focused on the design of output feedback controllers which guarantee the asymptotical stability of the closed-loop systems. Based on the model transformation of neutral type, the Lyapunov-Krasovskii functional method is employed to establish the delay-dependent stability criterion. Then, through the controller parameterization and some matrix transformation techniques, the desired parameters are determined under the delay-dependent design condition in terms of linear matrix inequalities (LMIs), and the desired controller is explicitly formulated. A numerical example is given to illustrate the effectiveness of the proposed method.

Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations

Fixed-time synchronization(FTS)of delayed memristor-based neural networks(MNNs)with discontinuous activations is studied in this paper.Both continuous and discontinuous activations are considered forMNNs.And the mixed delays which are closer to reality are taken into the system.Besides,two kinds of control schemes are proposed,including feedback and adaptive control strategies.Based on some lemmas,mathematical inequalities and the designed controllers,a few synchronization criteria are acquired.Moreover,the upper bound of settling time(ST)which is independent of the initial values is given.Finally,the feasibility of our theory is attested by simulation examples.

Almost periodic solutions of memristive multidirectional associative memory neural networks with mixed time delays

Traditional biological neural networks cannot simulate the real situation of the abrupt synaptic connections between neurons while modeling associative memory of human brains.In this paper,the memristive multidirectional associative memory neural networks(MAMNNs)with mixed time-varying delays are investigated in the sense of Filippov solution.First,three steps are given to prove the existence of the almost periodic solution.Two new lemmas are proposed to prove the boundness of the solution and the asymptotical almost periodicity of the solution by constructing Lyapunov function.Second,the uniqueness and global exponential stability of the almost periodic solution of memristive MAMNNs are investigated by a new Lyapunov function.The sufficient conditions guaranteeing the properties of almost periodic solution are derived based on the relevant definitions,Halanay inequality and Lyapunov function.The investigation is an extension of the research on the periodic solution and almost periodic solution of bidirectional associative memory neural networks.Finally,numerical examples with simulations are presented to show the validity of the main results.

H_-/H_∞ fault detection filter design for interval time-varying delays switched systems

The problem of the robust fault detection filter design for time-varying delays switched systems is considered in the framework of mixed H-/H∞. Firstly, the weighted H∞ performance index is utilized as the robustness performance, and the H- index is used as the sensitivity performance for obtaining the robust fault detection filter. Then a novel multiple Lyapunov-Krasovskii function is proposed for deriving sufficient existence conditions of the robust fault detection filter based on the average dwell time technique. By introducing slack matrix variable, the coupling between the Lyapunov matrix and system matrix is removed, and the conservatism of results is reduced. Based on the robust fault detection filter, residual is generated and evaluated for detecting faults. In addition, the results of this paper are dependent on time delays,and represented in the form of linear matrix inequalities. Finally,the simulation example verifies the effectiveness of the proposed method.

Scaled Bipartite Consensus Controller Design for Second-Order Multi-Agent Systems with Mixed Time-Delays

The scaled bipartite consensus of second-order multi-agent systems is investigated in this paper.The internal delay and distributed delay are also considered in the dynamic model of each agent,in which the delays can be time-varying and large.The communication topology among agents is assumed to be directed and structurally balanced.On one hand,in order to guarantee scaled bipartite consensus of second-order multi-agent systems,an adaptive periodically intermittent control protocol is applied.On the other hand,some consensus criteria in the form of matrix inequalities are obtained by using Jensen inequality,Lyapunov stability theory and graph theory.Finally,a numerical simulation example is given to demonstrate the feasibility of theoretical results.

General Decay Synchronization for Recurrent Neural Networks with Mixed Time Delays

This paper studies the general decay synchronization(GDS)of a class of recurrent neural networks(RNNs)with general activation functions and mixed time delays.By constructing suitable Lyapunov-Krasovskii functionals and employing useful inequality techniques,some sufficient conditions on the GDS of considered RNNs are established via a type of nonlinear control.In addition,one example with numerical simulations is presented to illustrate the obtained theoretical results.

Exponential synchronization for neural networks with mixed time-varying delays via periodically intermittent control

This paper deals with exponential synchronization for a class of neural networks with mixed time-varying delays via periodically intermittent control. Some novel and effective exponential synchronization criteria are derived by constructing Lyapunov functional and applying some analysis techniques. These results presented in this paper generalize and improve many known results. Finally, this paper presents an illustrative example and uses the simulated results to show the feasibility and effectiveness of the proposed scheme.

STABILITY IN LAGRANGE SENSE FOR A CLASS OF STOCHASTIC STATIC NEURAL NETWORKS WITH MIXED TIME DELAYS

In this paper, the stability in Lagrange sense of a class of stochastic static neural networks with mixed time delays is studied. Based on the Lyapunov stability theory and with the help of stochastic analysis technique, the criteria for the stability in Lagrange sense of stochastic static neural networks with mixed time delays is obtained. One example is given to verify the advantage and applicability of the proposed results.