Minghui Jiang

Global stability of periodic solution for bidirectional associative memory neural networks with varying-time delay

This paper investigates the global stability of periodic solution for bidirectional associative memory neural networks by using linear matrix inequality, and presents new sufficient conditions on the existence, uniqueness, global exponential stability and asymptotic stability of periodic solution for bidirectional associative memory neural networks with varying-time delays. In addition, exponential convergence rate is estimated by the equation in the paper. Furthermore, the results in this paper are generalized and the ones reported in the existing literatures are improved. Numerical simulations are given to verify the effectiveness of our main results.

Finite-Time boundedness analysis of uncertain CGNNs with multiple delays

The problem of finite-time boundedness(FTB) for a class of Cohen-Grossberg neural networks(CGNNs) with multiple delays and parameter perturbations is analyzed in this paper By way of extending the concept of FTB for time delay system and constructing a suitable Lyapunov function and using linear matrix inequality(LMI) technique, some delay-dependent criteria are derived to guarantee FTB for uncertain and certain CGNNs with multiple delays Meanwhile, an algorithm is also presented Finally, simulation examples are given to demonstrate the effectiveness of the conclusion.

A Modified Projection Neural Network for Linear Variational Inequalities and Quadratic Optimization Problems

Variational inequalities provide us with a tool to study a wide class of optimization arising in pure and applied sciences. In the paper,we present a neural network for solving linear variational inequalities and quadratic optimization by using a projection techniques. We also consider the global uniqueness of the solution of the neural network as well as the convergence of the modified projection neural network. Our results present a significant improvement of previously known projection methods for solving variational inequalities and related optimization problems. Two simulation examples are provided to show the effectiveness of the approach and applicability of the proposed criteria.

Nonlinear behavior of the parameterized Logistic differential systems

This paper deals with local stability, Hopf bifurcation and chaos in the parameterized Logistic differential systems with delay. By applying the Halanay inequality, the local stability of the Logistic differential systems is discussed. The stability of bifurcation periodic solutions and the direction of Hopf bifurcation are determined by applying the normal form theory and the center manifold theorem. In the end, interesting nonlinear behavior of the parameterized Logistic differential systems with a single parameter delay is detected by numerical examples.

H ∞ synchronization control in nonlinear time-delay complex dynamical network

On the basis of Lyapunov stability theory and LMI technique, this paper investigates H∞ synchronization control of time-varying synchronization state in general complex networks with time-delay and external disturbances. H∞ synchronization controllers have been designed for nodes of controlled network and the H∞ control law for asymptotically (exponentially) synchronization are derived via defining an appropriate controlled output. Besides, under the desired H∞ performance index, the control criteria which guarantee complex networks synchronization with a time-varying state are revealed by LMI. Finally, a simulation example is given to demonstrate the effectiveness of the theoretical results.

Adaptive synchronization of complex dynamical networks with coupling delays and multi-links

The paper is mainly concerned with the issues of synchronization dynamics of complex dynamical networks with coupling delays and multi-links. A general model of complex networks with multi-links is formulated, which can well describe practical architectures of more realistic complex networks related to multi-links. Based on Lyapunov stability theory and adaptive control techniques, some simple controllers with updated feedback strength are introduced to make the network locally and globally synchronized. Furthermore, the effectiveness of theoretical results are verified by numerical simulations.

On global asymptotic stability criteria for cellular neural networks with discrete and distributed time-varying delays

In this paper, the global asymptotic stability for a class of uncertain delayed cellular neural networks with discrete and distributed time-varying delays (DCNNs) is considered. Based on the Lyapunov functional stability analysis for differential equations and the linear matrix inequality (LMI) optimization approach, a new criterion is derived to guarantee global asymptotic stability. A numerical example is illustrated to show the effectiveness of our results.

Properties of periodic solutions for common logistic model with discrete and distributed delay

This paper discusses about the existence and uniqueness of periodic solutions of Logistic model with discrete and distributed delay By using a Lyapunov function which unlike the former method, the conditions of existence and uniqueness of periodic solutions for this Logistic model are obtained The result of this paper extend results of periodic solutions for the delay differential equations.

Global and Local Synchronization of General Multi-linked Delayed Complex Dynamical Networks

This paper investigates the phenomena of a new and general multi-linked complex network with delayed nodes and couplings. Via constructing Lyapunov-Krasovskii function and using inequality technique, the criteria for locally exponential synchronization and globally asymptotical synchronization are derived. The latter neither requires the outer-coupling configuration matrix to be symmetric and irreducible nor needs the network to be linearized at its synchronization state, it is obtained by Kronecker product method. Finally, a numerical example illustrates the theoretical results.

Asymptotic behavior of stochastic Cohen-Grossberg neural networks with variable delays

Using Chebyshev inequality and nonnegative semi-martingale convergence theorem, the paper investigates asymptotic behavior of stochastic Cohen-Grossberg neural networks with delay by constructing suitable Lyapunov functional. Algebraic criteria are given for stochastic ultimate bounded and almost exponential stability. The result in the paper extend the main conclusion. In the end, examples are given to verify the effective of our results.