Biomedical subjects

Zidong Wang

Publications and source records attributed to Zidong Wang.

4 recordsLinked to original sources

PubMed2006

Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays.

In this letter, the global asymptotic stability analysis problem is considered for a class of stochastic Cohen-Grossberg neural networks with mixed time delays, which consist of both the discrete and distributed time delays. Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, a linear matrix inequality (LMI) approach is developed to derive several sufficient conditions guaranteeing the global asymptotic convergence of the equilibrium point in the mean square. It is shown that the addressed stochastic Cohen-Grossberg neural networks with mixed delays are globally asymptotically stable in the mean square if two LMIs are feasible, where the feasibility of LMIs can be readily checked by the Matlab LMI toolbox. It is also pointed out that the main results comprise some existing results as special cases. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria.

Algorithms↗

PubMed2005

Global exponential stability of generalized recurrent neural networks with discrete and distributed delays.

This paper is concerned with analysis problem for the global exponential stability of a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. We first prove the existence and uniqueness of the equilibrium point under mild conditions, assuming neither differentiability nor strict monotonicity for the activation function. Then, by employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally exponentially stable. Therefore, the global exponential stability of the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.

Artificial Intelligence↗

PubMed2005

Observer design and stabilization for linear neutral delay systems.

This paper focuses on the state observer design problem as well as the observer-based stabilization problem for linear neutral delay systems. The purpose of the former problem is to design an observer that guarantees the asymptotic stability of the estimation error dynamics. The existence condition for such an observer is established. The latter problem, which is the main problem studied in this paper, aims at designing an observer-based feedback controller, such that the closed-loop system is asymptotically stabilized. It is shown that the desired controller can be easily designed if there are solutions to several linear matrix inequalities. Finally, two simulation examples are given to demonstrate the validity and effectiveness of the proposed approach.

Journal Article↗

PubMed2005

State estimation for delayed neural networks.

In this letter, the state estimation problem is studied for neural networks with time-varying delays. The interconnection matrix and the activation functions are assumed to be norm-bounded. The problem addressed is to estimate the neuron states, through available output measurements, such that for all admissible time-delays, the dynamics of the estimation error is globally exponentially stable. An effective linear matrix inequality approach is developed to solve the neuron state estimation problem. In particular, we derive the conditions for the existence of the desired estimators for the delayed neural networks. We also parameterize the explicit expression of the set of desired estimators in terms of linear matrix inequalities (LMIs). Finally, it is shown that the main results can be easily extended to cope with the traditional stability analysis problem for delayed neural networks. Numerical examples are included to illustrate the applicability of the proposed design method.

Algorithms↗