A combined reciprocal convexity approach for stability analysis of static neural networks with interval time-varying delays

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• 作者：Won Il Lee<sup>asup>Author Vitae ; Seok Young Lee<sup>bsup>Author Vitae ; PooGyeon Park<sup>asup><sup> ; sup><sup>ppg@postech.ac.krsup>Author Vitae
• 关键词：Reciprocal convexity approach ; Interval time-varying delays ; Stability analysis ; Static neural networks
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：19 January 2017
• 年：2017
• 卷：221
• 期：Complete
• 页码：168-177
• 全文大小：477 K
• 卷排序：221

文摘

This paper proposes a novel approach called a combined reciprocal convexity approach for the stability analysis of static neural networks with interval time-varying delays. The proposed approach deals with all convex-parameter-dependent terms in the time derivative of the Lyapunov-Krasovskii functional non-conservatively by extending the idea of the conventional reciprocal convexity approach. Based on the proposed technique and a new Lyapunov-Krasovskii functional, two improved delay-dependent stability criteria are derived in terms of linear matrix inequalities(LMIs). Some numerical examples are given to demonstrate the proposed results.