New results for global robust asymptotic stability of BAM neural networks with time-varying delays

Authors:
Yufa Yuan;Xiaolin Li
Affiliations:
Department of Mathematics, Shanghai University, Shanghai 200444, PR China;Department of Mathematics, Shanghai University, Shanghai 200444, PR China
Venue:
Neurocomputing
Year:
2010

Citing 8
Cited 1

Topics in matrix analysis

Topics in matrix analysis
Bidirectional associative memories

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New delay-dependent exponential stability criteria of BAM neural networks with time delays

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Global asymptotic stability analysis of bidirectional associative memory neural networks with constant time delays

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Global Robust Stability of Bidirectional Associative Memory Neural Networks With Multiple Time Delays

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BAM-type Cohen-Grossberg neural networks with time delays

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Global asymptotic stability analysis of bidirectional associative memory neural networks with time delays

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A new robust stability criterion for dynamical neural networks with multiple time delays

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Abstract

In this paper, the global robust asymptotic stability for a class of delayed bidirectional associative memory (BAM) neural networks with interval uncertainty is studied. Some less conservative conditions are presented for the BAM neural networks with multiple time-varing delays based on the Lyapunov functional approach. We also give one example to demonstrate the applicability and effectiveness of our results, and compare the results with the previous robust stability results derived in the literature.