An analysis of global exponential stability of bidirectional associative memory neural networks with constant time delays

Authors:
Weirui Zhao;HuanShui Zhang;Shulan Kong
Affiliations:
Shenzhen Graduate School, Harbin Institute of Technology, HIT Campus of ShenZhen University Town, Xili, Shenzhen 518055, PR China and Department of Mathematics, Wuhan University of Technology, 122 ...;Shenzhen Graduate School, Harbin Institute of Technology, HIT Campus of ShenZhen University Town, Xili, Shenzhen 518055, PR China;School of Mathematical Sciences, Qufu Normal University, Qufu, Shandong 273165, PR China
Venue:
Neurocomputing
Year:
2007

Citing 8
Cited 4

Exponential stability of delayed bi-directional associative memory networks

Applied Mathematics and Computation
Global stability of cellular neural networks with constant and variable delays

Nonlinear Analysis: Theory, Methods & Applications
An analysis of exponential stability of delayed neural networks with time varying delays

Neural Networks
Analysis of global exponential stability and periodic solutions of neural networks with time-varying delays

Neural Networks
Global exponential stability of a class of neural networks with variable delays

Computers & Mathematics with Applications
Global asymptotic stability analysis of bidirectional associative memory neural networks with constant time delays

Neurocomputing
Exponential stability and periodic oscillatory solution in BAM networks with delays

IEEE Transactions on Neural Networks
Global asymptotic stability analysis of bidirectional associative memory neural networks with time delays

IEEE Transactions on Neural Networks

Letters: State estimation for discrete-time neural networks with time-varying delays

Neurocomputing
New delay-dependent exponential stability criteria of BAM neural networks with time delays

Mathematics and Computers in Simulation
Some multistability properties of bidirectional associative memory recurrent neural networks with unsaturating piecewise linear transfer functions

Neurocomputing
Original article: LMI criteria on exponential stability of BAM neural networks with both time-varying delays and general activation functions

Mathematics and Computers in Simulation

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents a new sufficient condition for the existence, uniqueness and global exponential stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with constant delays. The results are also compared with the previous results in the literature, implying that the results obtained in this paper provide one more set of criteria for determining the global exponential stability of BAM neural networks with constant delays.