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
收藏
页码：161 / 165
页数：5
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