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Stability of stochastic fuzzy BAM neural networks with discrete and distributed time-varying delays

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Abstract

Among the various fuzzy models, the well-known Takagi–Sugeno (T–S) fuzzy model is recognized as a popular and powerful tool in approximating a complex nonlinear system. T–S model provides a fixed structure to some nonlinear systems and facilitates the analysis of the system. This paper deals with the global stability of stochastic bidirectional associative memory (BAM) neural networks with discrete and distributed time-varying delays which are represented by the T–S fuzzy models. The stability conditions are derived using Lyapunov–Krasovskii functional combined with the linear matrix inequality (LMI) techniques. Finally, numerical examples are given to demonstrate the correctness of the theoretical results.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu, India

    M. Syed Ali

  2. Department of Mathematics, Gandhigram Rural University, Dindigul, Tamilnadu, India

    P. Balasubramaniam

  3. School of Mathematical Sciences and Institute of Finance and Statistics, Nanjing Normal University, Jiangsu, China

    Quanxin Zhu

Authors
  1. M. Syed Ali
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  2. P. Balasubramaniam
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  3. Quanxin Zhu
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Corresponding authors

Correspondence to M. Syed Ali or Quanxin Zhu.

Additional information

The work was jointly supported by Department of Science and Technology (DST), under research project No.SR/FTP/MS-039/2011, the National Natural Science Foundation of China (61374080), the Natural Science Foundation of Zhejiang Province (LY12F03010), the Natural Science Foundation of Ningbo (2012A610032) and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Syed Ali, M., Balasubramaniam, P. & Zhu, Q. Stability of stochastic fuzzy BAM neural networks with discrete and distributed time-varying delays. Int. J. Mach. Learn. & Cyber. 8, 263–273 (2017). https://doi.org/10.1007/s13042-014-0320-7

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  • DOI: https://doi.org/10.1007/s13042-014-0320-7

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