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Stability results for Takagi–Sugeno fuzzy uncertain BAM neural networks with time delays in the leakage term

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Abstract

This paper deals with the delay-dependent asymptotic stability analysis problem for a class of fuzzy bidirectional associative memory (BAM) neural networks with time delays in the leakage term by Takagi–Sugeno (T–S) fuzzy model. The nonlinear delayed BAM neural networks are first established as a modified T–S fuzzy model in which the consequent parts are composed of a set of BAM neural networks with time-varying delays. The parameter uncertainties are assumed to be norm bounded. Some new delay-dependent stability conditions are derived in terms of linear matrix inequality by constructing a new Lyapunov–Krasovskii functional and introducing some free-weighting matrices. Even there is no leakage delay, the obtained results are also less restrictive than some recent works. It can be applied to BAM neural networks with activation functions without assuming their boundedness, monotonicity, or differentiability. Numerical examples are given to demonstrate the effectiveness of the proposed methods.

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Authors and Affiliations

  1. School of Mathematical Sciences, Shandong Normal University, Jinan, 250014, People’s Republic of China

    Xiaodi Li

  2. Department of Mathematics, Bharathiar University, Coimbatore, 641 046, India

    R. Rakkiyappan

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  1. Xiaodi Li
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  2. R. Rakkiyappan
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Correspondence to Xiaodi Li.

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Li, X., Rakkiyappan, R. Stability results for Takagi–Sugeno fuzzy uncertain BAM neural networks with time delays in the leakage term. Neural Comput & Applic 22 (Suppl 1), 203–219 (2013). https://doi.org/10.1007/s00521-012-0839-z

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  • DOI: https://doi.org/10.1007/s00521-012-0839-z

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