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

543Citations
Citations of this article
35Readers
Mendeley users who have this article in their library.
Get full text

Abstract

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. © 2005 Elsevier Ltd. All rights reserved.

Cite

CITATION STYLE

APA

Liu, Y., Wang, Z., & Liu, X. (2006). Global exponential stability of generalized recurrent neural networks with discrete and distributed delays. Neural Networks, 19(5), 667–675. https://doi.org/10.1016/j.neunet.2005.03.015

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free