Extended Dissipative Analysis for Neural Networks With Time-Varying Delays

被引：173

作者：

Lee, Tae H. ^{[1
]}

Park, Myeong-Jin ^{[2
]}

Park, Ju H. ^{[1
]}

Kwon, Oh-Min ^{[2
]}

Lee, Sang-Moon ^{[3
]}

机构：

[1] Yeungnam Univ, Dept Elect Engn, Nonlinear Dynam Grp, Kyongsan 712749, South Korea

[2] Chungbuk Natl Univ, Sch Elect Engn, Cheongju 361763, South Korea

[3] Daegu Univ, Sch Elect Engn, Kyongsan 712714, South Korea

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2014年 / 25卷 / 10期

基金：

新加坡国家研究基金会;

关键词：

Dissipativity; neural networks; passivity; stability; STABILITY; SYSTEMS;

D O I：

10.1109/TNNLS.2013.2296514

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this brief, an extended dissipativity analysis was conducted for a neural network with time-varying delays. The concept of the extended dissipativity can be used to solve for the H infinity, L-2-L-infinity, passive, and dissipative performance by adjusting the weighting matrices in a new performance index. In addition, the activation function dividing method is modified by introducing a tuning parameter. Examples are provided to show the effectiveness and less conservatism of the proposed method.

引用

页码：1936 / 1941

页数：6

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