Multistability analysis for a general class of delayed Cohen-Grossberg neural networks

Authors:
Zhenkun Huang;Chunhua Feng;Sannay Mohamad
Affiliations:
School of Science, Jimei University, Xiamen 361021, Fujian, China;College of Mathematical Science, Guangxi Normal University, Guilin 541004, Guangxi, China;Department of Mathematics, Faculty of Science, Universiti Brunei Darussalam, Gadong BE 1410, Brunei Darussalam
Venue:
Information Sciences: an International Journal
Year:
2012

Citing 9
Cited 1

Original Contribution: Associative memory with nonmonotone dynamics

Neural Networks
New conditions on global stability of Cohen-Grossberg neural networks

Neural Computation
Criteria for exponential stability of Cohen-Grossberg neural networks

Neural Networks
Stability of Cohen-Grossberg neural networks with time-varying delays

Neural Networks
New results on delay-dependent robust stability criteria of uncertain fuzzy systems with state and input delays

Information Sciences: an International Journal
A systematic approach to improve multiple Lyapunov function stability and stabilization conditions for fuzzy systems

Information Sciences: an International Journal
Convergence analysis of general neural networks under almost periodic stimuli

International Journal of Circuit Theory and Applications
Robust global exponential stability of Cohen-Grossberg neural networks with time delays

IEEE Transactions on Neural Networks
Multiperiodicity of Discrete-Time Delayed Neural Networks Evoked by Periodic External Inputs

IEEE Transactions on Neural Networks

Global exponential stability of almost periodic solution of delayed neural networks with discontinuous activations

Information Sciences: an International Journal

Quantified Score

Hi-index	0.07

Visualization

Abstract

In this paper, by discussing parameter conditions based on properties of activation functions, we decompose state space into positively invariant sets and establish sufficient conditions for the existence of locally stable equilibria for delayed Cohen-Grossberg neural networks (CGNNs) through Cauchy convergence principle. Some new criteria are derived for ensuring equilibria (periodic orbits) to be locally or globally exponentially stable in any designated region. Finally, our results are demonstrated by four numerical simulations.