1. Global exponential stability for interval general bidirectional associative memory (BAM) neural networks with proportional delays
- Author
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Changjin Xu, Yicheng Pang, and Peiluan Li
- Subjects
Equilibrium point ,0209 industrial biotechnology ,Artificial neural network ,General Mathematics ,General Engineering ,Fixed-point theorem ,02 engineering and technology ,Interval (mathematics) ,Nonlinear system ,020901 industrial engineering & automation ,Exponential stability ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,Applied mathematics ,020201 artificial intelligence & image processing ,Bidirectional associative memory ,Uniqueness ,Mathematics - Abstract
This paper is concerned with interval general bidirectional associative memory (BAM) neural networks with proportional delays. Using appropriate nonlinear variable transformations, the interval general BAM neural networks with proportional delays can be equivalently transformed into the interval general BAM neural networks with constant delays. The sufficient condition for the existence and uniqueness of equilibrium point of the model is established by applying Brouwer's fixed point theorem. By constructing suitable delay differential inequalities, some sufficient conditions for the global exponential stability of the model are obtained. Two examples are given to illustrate the effectiveness of the obtained results. This paper ends with a brief conclusion. Copyright © 2016 John Wiley & Sons, Ltd.
- Published
- 2016