Robust stability of uncertain fuzzy Cohen–Grossberg BAM neural networks with time-varying delays

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• 作者：M. Syed Ali ; P. Balasubramaniam
• 关键词：Fuzzy Cohen– ; Grossberg BAM neural networks (FCGBAMNNs) ; Global asymptotic stability ; Linear matrix inequality ; Lyapunov functional ; Time-varying delays
• 刊名：Expert Systems with Applications
• 出版年：2009
• 出版时间：September 2009
• 年：2009
• 卷：36
• 期：7
• 页码：10583-10588
• 全文大小：226 K

文摘

In this paper, the Takagi–Sugeno (TS) fuzzy model representation is extended to the stability analysis for uncertain Cohen–Grossberg type bidirectional associative memory (BAM) neural networks with time-varying delays using linear matrix inequality (LMI) theory. A novel LMI-based stability criterion is obtained by using LMI optimization algorithms to guarantee the asymptotic stability of uncertain Cohen–Grossberg BAM neural networks with time varying delays which are represented by TS fuzzy models. Finally, the proposed stability conditions are demonstrated with numerical examples.