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Exponential convergence rate estimation for neutral BAM neural networks with mixed time-delays

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Abstract

This paper is concerned with the exponential stability analysis problem for a class of neutral bidirectional associative memory neural networks with mixed time-delays, where discrete, distributed and neutral delays are involved. By utilizing the delay decomposition approach and an appropriately constructed Lyapunov–Krasovskii functional, some novel delay-dependent and decay rate-dependent criteria for the exponential stability of the considered neural networks are derived and presented in terms of linear matrix inequalities. Furthermore, the maximum allowable decay rate can be estimated based on the obtained results. Three numerical examples are given to demonstrate the effectiveness of the proposed method.

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Acknowledgments

This work was supported in part by the National Science Foundation of China under Grant 60974017, and in part by the Specialized Research Fund for the Doctoral Program of High Education, China under Grant 200803370002.

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Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Bo Chen, Li Yu & Wen-An Zhang

  2. Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, 310023, People’s Republic of China

    Bo Chen, Li Yu & Wen-An Zhang

Authors
  1. Bo Chen
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  2. Li Yu
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  3. Wen-An Zhang
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Correspondence to Li Yu.

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Chen, B., Yu, L. & Zhang, WA. Exponential convergence rate estimation for neutral BAM neural networks with mixed time-delays. Neural Comput & Applic 20, 451–460 (2011). https://doi.org/10.1007/s00521-010-0415-3

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  • DOI: https://doi.org/10.1007/s00521-010-0415-3

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