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Dynamics of fuzzy impulsive bidirectional associative memory neural networks with time-varying delays

  • Applied mathematics
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Abstract

Complex nonlinear systems can be represented to a set of linear sub-models by using fuzzy sets and fuzzy reasoning via ordinary Takagi-Sugeno (TS) fuzzy models. In this paper, the exponential stability of TS fuzzy bidirectional associative memory (BAM) neural networks with impulsive effect and time-varying delays is investigated. The model of fuzzy impulsive BAM neural networks with time-varying delays established as a modified TS fuzzy model is new in which the consequent parts are composed of a set of impulsive BAM neural networks with time-varying delays. Further the exponential stability for fuzzy impulsive BAM neural networks is presented by utilizing the Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) technique without tuning any parameters. In addition, an example is provided to illustrate the applicability of the result using LMI control toolbox in MATLAB.

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Authors and Affiliations

  1. Department of Mathematics, Bharathiar University, Coimbatore, 641 046, Tamilnadu, India

    R. Rakkiyappan

  2. School of Mathematical Sciences, Shandong Normal University, Ji’nan, 250014, P.R. China

    Xiaodi Li

  3. Department of Mathematics, National University of Ireland, Galway, Ireland

    Donal O’Regan

Authors
  1. R. Rakkiyappan
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  2. Xiaodi Li
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  3. Donal O’Regan
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Correspondence to Xiaodi Li.

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Rakkiyappan, R., Li, X. & O’Regan, D. Dynamics of fuzzy impulsive bidirectional associative memory neural networks with time-varying delays. J. Appl. Math. Comput. 40, 289–317 (2012). https://doi.org/10.1007/s12190-012-0554-z

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  • DOI: https://doi.org/10.1007/s12190-012-0554-z

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