Global Exponential Stability Criteria for Bidirectional Associative Memory Neural Networks with Time-Varying Delays-Reference-Cited by-同舟云学术

Global Exponential Stability Criteria for Bidirectional Associative Memory Neural Networks with Time-Varying Delays

Published:2013 Issue: Volume:2013 Page:1-13
ISSN:1085-3375
Container-title:Abstract and Applied Analysis
language:en
Short-container-title:Abstract and Applied Analysis

Author:

Thipcha J.¹,Niamsup P.¹²

Affiliation:

1. Department of Mathematics, Chiang Mai University, Chiang Mai 50200, Thailand

2. Centre of Excellence in Mathematics CHE, Si Ayutthaya Road, Bangkok 10400, Thailand

Abstract

The global exponential stability for bidirectional associative memory neural networks with time-varying delays is studied. In our study, the lower and upper bounds of the activation functions are allowed to be either positive, negative, or zero. By constructing new and improved Lyapunov-Krasovskii functional and introducing free-weighting matrices, a new and improved delay-dependent exponential stability for BAM neural networks with time-varying delays is derived in the form of linear matrix inequality (LMI). Numerical examples are given to demonstrate that the derived condition is less conservative than some existing results given in the literature.

Publisher

Hindawi Limited

Subject

Applied Mathematics,Analysis

Link

http://downloads.hindawi.com/journals/aaa/2013/576721.pdf

Reference23 articles.

1. Adaptive bidirectional associative memories

2. Global Robust Exponential Stability of Interval BAM Neural Network with Mixed Delays under Uncertainty

3. Novel stability criteria for bidirectional associative memory neural networks with time delays

4. Global dynamics of bidirectional associative memory neural networks involving transmission delays and dead zones

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