Journal
PHYSICS LETTERS A
Volume 317, Issue 1-2, Pages 97-106Publisher
ELSEVIER
DOI: 10.1016/j.physleta.2003.08.019
Keywords
periodic solution; BAM neural networks; coincidence degree
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In this Letter, we study BAM (bidirectional associative memory) networks with variable coefficients. By some spectral theorems and a continuation theorem based on coincidence degree, we not only obtain some new sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the periodic solution but also estimate the exponentially convergent rate. Our results are less restrictive than previously known criteria and can be applied to neural networks with a broad range of activation functions assuming neither differentiability nor strict monotonicity. Moreover, these conclusions are presented in terms of system parameters and can be easily verified for the globally Lipschitz and the spectral radius being less than 1. Therefore, our results should be useful in the design and applications of periodic oscillatory neural circuits for neural networks with delays. (C) 2003 Elsevier B.V. All rights reserved.
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