Robust stability of mixed Cohen–Grossberg neural networks with discontinuous activation functions

Abstract

Purpose

The purpose of this paper is to develop a method for the existence, uniqueness and globally robust stability of the equilibrium point for Cohen–Grossberg neural networks with time-varying delays, continuous distributed delays and a kind of discontinuous activation functions.

Design/methodology/approach

Based on the Leray–Schauder alternative theorem and chain rule, by using a novel integral inequality dealing with monotone non-decreasing function, the authors obtain a delay-dependent sufficient condition with less conservativeness for robust stability of considered neural networks.

Findings

It turns out that the authors’ delay-dependent sufficient condition can be formed in terms of linear matrix inequalities conditions. Two examples show the effectiveness of the obtained results.

Originality/value

The novelty of the proposed approach lies in dealing with a new kind of discontinuous activation functions by using the Leray–Schauder alternative theorem, chain rule and a novel integral inequality on monotone non-decreasing function.

Citation

Zheng, C.-D., Liu, Y. and Xiao, Y. (2019), "Robust stability of mixed Cohen–Grossberg neural networks with discontinuous activation functions", International Journal of Intelligent Computing and Cybernetics, Vol. 12 No. 1, pp. 82-101. https://doi.org/10.1108/IJICC-08-2018-0105