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

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

This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations are well-constrained Moreover, the present method can also be applied to general differential systems with stochastic perturbation and impulses

Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects

Sufficient conditions are obtained for the existence and global exponential stability of a unique equilibrium of a class of two-layer heteroassociative networks called bidirectional associative memory (BAM) networks with Lipschitzian activation functions without assuming their boundedness, monotonicity or differentiability and subjected to impulsive state displacements at fixed instants of time. An illustrative example is given to demonstrate the effectiveness of the obtained results.

Global exponential stability of BAM neural networks with delays and impulses

Global stability of complex-valued neural networks with both leakage time delay and discrete time delay on time scales

This letter provides a brief explanation of echo state networks (ESNs) and provides a rigorous bound for guaranteeing asymptotic stability of these networks. The stability bounds presented here could aid in the design of echo state networks that would be applicable to control applications where stability is required

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A tighter bound for the echo state property

Periodic Solutions of Periodic Delay Lotka–Volterra Equations and Systems☆

In this paper, we consider the existence and multiplicity of solutions for some Dirichlet impulsive problems and some existence and multiplicity results are obtained. The solutions are sought by means of Lax–Milgram theorem and some critical theorems.

Existence and multiplicity of solutions for some Dirichlet problems with impulsive effects

Global exponential stability and existence of periodic solution of Hopfield-type neural networks with impulses

We use the continuation theorem of coincidence degree theory and Liapunov functions to study the existence and stability of periodic solutions for the Cohen–Grossberg neural network with multiple delays.

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Yongkun Li

Papers

Global exponential stability of BAM neural networks with delays and impulses

Periodic Solutions of Periodic Delay Lotka–Volterra Equations and Systems☆

Existence and multiplicity of solutions for some Dirichlet problems with impulsive effects

Global exponential stability and existence of periodic solution of Hopfield-type neural networks with impulses

Existence and stability of periodic solutions for Cohen–Grossberg neural networks with multiple delays