Existence and globally exponential stability of equilibrium for fuzzy BAM neural networks with distributed delays and impulse

Qianhong Zhang,Lihui Yang,Daixi Liao

doi:10.1186/1687-1847-2011-8

Abstract

In this article, fuzzy bi-directional associative memory neural networks with distributed delays and impulses are considered. Some sufficient conditions for the existence and globally exponential stability of unique equilibrium point are established using fixed point theorem and differential inequality techniques. The results obtained are easily checked to guarantee the existence, uniqueness, and globally exponential stability of equilibrium point. MSC: 34K20; 34K13; 92B20

Highlights

The bidirectional associative memory neural networks (BAM) models were first introduced by Kosko [1,2]
It is a special class of recurrent neural networks that can store bipolar vector pairs
Many researchers have studied the dynamics of BAM neural networks with or without delays [1-23] including stability and periodic solutions

Summary

Introduction

The bidirectional associative memory neural networks (BAM) models were first introduced by Kosko [1,2] It is a special class of recurrent neural networks that can store bipolar vector pairs. Through iterations of forward and backward information flows between the two layers, it performs two-way associative search for stored bipolar vector pairs and generalize the single-layer autoassociative Hebbian correlation to twolayer pattern-matched heteroassociative circuits. This class of networks possesses a good applications prospects in the areas of pattern recognition, signal and image process, automatic control.

Objectives

Conclusion