New approaches on stability criteria for neural networks with interval time-varying delays

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• 作者：O.M. Kwon^a ; ^{madwind@chungbuk.ac.kr} ; S.M. Lee^b ; ^{moony@daegu.ac.kr} ; Ju H. Park^c ; ^{jessie@ynu.ac.kr} ; E.J. Cha^d ; ^{ejcha@chungbuk.ac.kr}
• 关键词：Neural networks ; Time-varying delays ; Stability ; Lyapunov method
• 刊名：Applied Mathematics and Computation
• 出版年：2012
• 期刊代码：7_00963003
• 类别：cp
• 出版时间：1 June, 2012
• 卷：218
• 期：19
• 页码：9953-9964
• 文件大小：289 K

摘要

This paper concerns the problem of delay-dependent stability criteria for neural networks with interval time-varying delays. First, by constructing a newly augmented Lyapunov-Krasovskii functional and combining with a reciprocally convex combination technique, less conservative stability criterion is established in terms of linear matrix inequalities (LMIs), which will be introduced in . Second, by taking different interval of integral terms of Lyapunov-Krasovskii functional utilized in , further improved stability criterion is proposed in . Third, a novel approach which divides the bounding of activation function into two subinterval are proposed in to reduce the conservatism of stability criterion. Finally, through two well-known numerical examples used in other literature, it will be shown the proposed stability criteria achieves the improvements over the existing ones and the effectiveness of the proposed idea.