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Global exponential stability for interval general bidirectional associative memory (BAM) neural networks with proportional delays
Author(s) -
Xu Changjin,
Li Peiluan,
Pang Yicheng
Publication year - 2016
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.3957
Subject(s) - mathematics , interval (graph theory) , uniqueness , artificial neural network , bidirectional associative memory , exponential stability , nonlinear system , equilibrium point , stability (learning theory) , fixed point theorem , content addressable memory , constant (computer programming) , variable (mathematics) , control theory (sociology) , differential equation , discrete mathematics , mathematical analysis , computer science , combinatorics , artificial intelligence , control (management) , physics , quantum mechanics , machine learning , programming language
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.