Multistability for Delayed Neural Networks via Sequential Contracting

被引：42

作者：

Cheng, Chang-Yuan ^{[1
]}

Lin, Kuang-Hui ^{[2
]}

Shih, Chih-Wen ^{[2
]}

Tseng, Jui-Pin ^{[3
]}

机构：

[1] Natl Pingtung Univ, Dept Appl Math, Pingtung 90003, Taiwan

[2] Natl Chiao Tung Univ, Dept Appl Math, Hsinchu 30010, Taiwan

[3] Natl Chengchi Univ, Dept Math Sci, Taipei 11605, Taiwan

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2015年 / 26卷 / 12期

关键词：

Complete stability; delay equations; multistability; neural network; COMPLETE STABILITY; PATTERN-FORMATION; SYNCHRONIZATION; DYNAMICS; NEURONS; LIMIT; MODEL;

D O I：

10.1109/TNNLS.2015.2404801

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 paper, we explore a variety of new multistability scenarios in the general delayed neural network system. Geometric structure embedded in equations is exploited and incorporated into the analysis to elucidate the underlying dynamics. Criteria derived from different geometric configurations lead to disparate numbers of equilibria. A new approach named sequential contracting is applied to conclude the global convergence to multiple equilibrium points of the system. The formulation accommodates both smooth sigmoidal and piecewise-linear activation functions. Several numerical examples illustrate the present analytic theory.

引用

页码：3109 / 3122

页数：14

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