Junjian Huang

Stabilization of Delayed Chaotic Neural Networks by Periodically Intermittent Control

This paper studies the exponential stabilization of delayed chaotic neural networks (DCNNs) using what is called periodically intermittent control. An exponential stability criterion for the controlled neural networks, together with its simplified version, is established by using the Lyapunov function and Halanay inequality. The feasible region of control parameters is estimated in a rigorous way. Theoretical results and numerical simulations show that the continuous-time DCNN can be stabilized by intermittent feedback control with nonzero duration.

A Recurrent Neural Network for Solving Bilevel Linear Programming Problem

In this brief, based on the method of penalty functions, a recurrent neural network (NN) modeled by means of a differential inclusion is proposed for solving the bilevel linear programming problem (BLPP). Compared with the existing NNs for BLPP, the model has the least number of state variables and simple structure. Using nonsmooth analysis, the theory of differential inclusions, and Lyapunov-like method, the equilibrium point sequence of the proposed NNs can approximately converge to an optimal solution of BLPP under certain conditions. Finally, the numerical simulations of a supply chain distribution model have shown excellent performance of the proposed recurrent NNs.

Robust stability of stochastic fuzzy delayed neural networks with impulsive time window

The urgent problem of impulsive moments which cannot be determined in advance brings new challenges beyond the conventional impulsive systems theory. In order to solve this problem, the novel concept of impulsive time window is proposed in this paper. And the stability problem of stochastic fuzzy uncertain delayed neural networks with impulsive time window is investigated. By combining the discretized Lyapunov function approach with mathematical induction method, several novel and easy-to-check sufficient conditions concerning the impulsive time window are derived to ensure that the model considered here is exponentially stable in mean square. Numerical simulations are presented to further demonstrate the effectiveness of the proposed stability criterion.

Anticipating synchronization of chaotic systems with time delay and parameter mismatch

This paper studies the effect of parameter mismatch on anticipating synchronization of chaotic systems with time delay in the framework of the master-slave configuration. The convergence criteria for the error dynamical system under study are established by means of model transformation incorporated with Lyapunov functional and linear matrix inequality. The error bound of anticipating synchronization is estimated by rigorous theoretical analysis. Its accuracy is confirmed by numerical simulation results.

Estimation on Error Bound of Lag Synchronization of Chaotic Systems with Time Delay and Parameter Mismatch

This paper studies the effect of parameter mismatch on lag synchronization of chaotic systems with time delay in the framework of master-slave configuration. It shall be shown that lag synchronization of coupled systems may achieve weakly in the presence of small parameter mismatches. The error bound of lag-synchronization arising from the parameter mismatches is also estimated by rigorously theoretical analysis. Numerical simulations on a Lu oscillator are presented to verify the theoretical results.This paper studies the effect of parameter mismatch on lag synchronization of chaotic systems with time delay in the framework of master-slave configuration. It shall be shown that lag synchronization of coupled systems may achieve weakly in the presence of small parameter mismatches. The error bound of lag-synchronization arising from the parameter mismatches is also estimated by rigorously theoretical analysis. Numerical simulations on a Lu oscillator are presented to verify the theoretical results.

Modeling Computer Virus and Its Dynamics

Based on that the computer will be infected by infected computer and exposed computer, and some of the computers which are in suscepitible status and exposed status can get immunity by antivirus ability, a novel coumputer virus model is established. The dynamic behaviors of this model are investigated. First, the basic reproduction number , which is a threshold of the computer virus spreading in internet, is determined. Second, this model has a virus-free equilibrium , which means that the infected part of the computer disappears, and the virus dies out, and is a globally asymptotically stable equilibrium if . Third, if then this model has only one viral equilibrium , which means that the computer persists at a constant endemic level, and is also globally asymptotically stable. Finally, some numerical examples are given to demonstrate the analytical results.

Projective Lag Synchronization of Delayed Neural Networks Using Intermittent Linear State Feedback

The problem of projective lag synchronization of coupled neural networks with time delay is investigated. By means of the Lyapunov stability theory, an intermittent controller is designed for achieving projective lag synchronization between two delayed neural networks systems. Numerical simulations on coupled Lu neural systems illustrate the effectiveness of the results.

Quasi-synchronization of Chaotic Neural Networks with Parameter Mismatch by Periodically Intermittent Control

This paper describes a method for the weak synchronization of chaotic systems with parameter mismatch by using periodically intermittent control. Because of the small parameter mismatch, the synchronization is not complete. Via intermittent control with periodically intervals, we can obtain the quasi-synchronization. Some sufficient conditions for the stabilization and quasi-synchronization of a large class of coupled chaotic systems will be derived by using Lyapunov stability theory. The analytical results are confirmed by numerical simulations.

Weak projective lag synchronization of neural networks with parameter mismatch

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

A feedback neural network for solving convex quadratic bi-level programming problems

In this paper, a feedback neural network model is proposed for solving a class of convex quadratic bi-level programming problems based on the idea of successive approximation. Differing from existing neural network models, the proposed neural network has the least number of state variables and simple structure. Based on Lyapunov theories, we prove that the equilibrium point sequence of the feedback neural network can approximately converge to an optimal solution of the convex quadratic bi-level problem under certain conditions, and the corresponding sequence of the function value approximately converges to the optimal value of the convex quadratic bi-level problem. Simulation experiments on three numerical examples and a portfolio selection problem are provided to show the efficiency and performance of the proposed neural network approach.