Further Results on Passivity Analysis of Neural Networks With Time-Varying Delay

被引：0

作者：

Zeng, Hong-Bing ^{[1
]}

Xiao, Shen-Ping ^{[1
]}

Zhang, Chang-Fan ^{[1
]}

Chen, Gang ^{[1
]}

机构：

[1] Hunan Univ Technol, Sch Elect & Informat Engn, Zhuzhou 412007, Peoples R China

来源：

26TH CHINESE CONTROL AND DECISION CONFERENCE (2014 CCDC) | 2014年

关键词：

Neural networks; passivity; delay-dependent; Lyapunov-Krasovskii functional; linear matrix inequalities (LMIs); GLOBAL EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; ROBUST STABILITY; DISCRETE; CRITERIA;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper deals with the problem of passivity analysis for neural networks with both time-varying delay and norm-bounded parameter uncertainties. By further utilizing the information of activation function and employing a reciprocally convex approach to consider the relationship between the time-varying delay and its time-varying interval, some improved delay-dependent passivity conditions are obtained, which are formulated in terms of linear matrix inequalities (LMIs) and can be readily solved by existing convex optimization algorithms. Finally, a numerical example is provided to verify the effectiveness of the proposed techniques.

引用

页码：161 / 165

页数：5

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