Multiple Lagrange stability and Lyapunov asymptotical stability of delayed fractional-order Cohen-Grossberg neural networks

doi:10.1088/1674-1056/ab6716

中国物理B ›› 2020, Vol. 29 ›› Issue (2): 20703-020703.doi: 10.1088/1674-1056/ab6716

• SPECIAL TOPIC—Recent advances in thermoelectric materials and devices • 上一篇下一篇

Multiple Lagrange stability and Lyapunov asymptotical stability of delayed fractional-order Cohen-Grossberg neural networks

Yu-Jiao Huang(黄玉娇), Xiao-Yan Yuan(袁孝焰), Xu-Hua Yang(杨旭华), Hai-Xia Long(龙海霞), Jie Xiao(肖杰)

1 Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, China;
2 College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

收稿日期:2019-06-24 修回日期:2019-11-06 出版日期:2020-02-05 发布日期:2020-02-05
通讯作者: Xu-Hua Yang E-mail:xhyang@zjut.edu.cn
基金资助:
Project supported by the Natural Science Foundation of Zhejiang Province of China (Grant Nos. LY18F030023, LY17F030016, LQ18F030015, and LY18F020028) and the National Natural Science Foundation of China (Grant Nos. 61503338, 61773348, and 61972354).

Multiple Lagrange stability and Lyapunov asymptotical stability of delayed fractional-order Cohen-Grossberg neural networks

Yu-Jiao Huang(黄玉娇)^1,2, Xiao-Yan Yuan(袁孝焰)², Xu-Hua Yang(杨旭华)², Hai-Xia Long(龙海霞)², Jie Xiao(肖杰)²

1 Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, China;
2 College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Received:2019-06-24 Revised:2019-11-06 Online:2020-02-05 Published:2020-02-05
Contact: Xu-Hua Yang E-mail:xhyang@zjut.edu.cn
Supported by:
Project supported by the Natural Science Foundation of Zhejiang Province of China (Grant Nos. LY18F030023, LY17F030016, LQ18F030015, and LY18F020028) and the National Natural Science Foundation of China (Grant Nos. 61503338, 61773348, and 61972354).

摘要/Abstract

摘要： This paper addresses the coexistence and local stability of multiple equilibrium points for fractional-order Cohen-Grossberg neural networks (FOCGNNs) with time delays. Based on Brouwer's fixed point theorem, sufficient conditions are established to ensure the existence of Π_i=1ⁿ(2K_i+1) equilibrium points for FOCGNNs. Through the use of Hardy inequality, fractional Halanay inequality, and Lyapunov theory, some criteria are established to ensure the local Lagrange stability and the local Lyapunov asymptotical stability of Π_i=1ⁿ(K_i+1) equilibrium points for FOCGNNs. The obtained results encompass those of integer-order Hopfield neural networks with or without delay as special cases. The activation functions are nonlinear and nonmonotonic. There could be many corner points in this general class of activation functions. The structure of activation functions makes FOCGNNs could have a lot of stable equilibrium points. Coexistence of multiple stable equilibrium points is necessary when neural networks come to pattern recognition and associative memories. Finally, two numerical examples are provided to illustrate the effectiveness of the obtained results.

关键词: fractional-order Cohen-Grossberg neural networks, multiple Lagrange stability, multiple Lyapunov asymptotical stability, time delays

Abstract: This paper addresses the coexistence and local stability of multiple equilibrium points for fractional-order Cohen-Grossberg neural networks (FOCGNNs) with time delays. Based on Brouwer's fixed point theorem, sufficient conditions are established to ensure the existence of Π_i=1ⁿ(2K_i+1) equilibrium points for FOCGNNs. Through the use of Hardy inequality, fractional Halanay inequality, and Lyapunov theory, some criteria are established to ensure the local Lagrange stability and the local Lyapunov asymptotical stability of Π_i=1ⁿ(K_i+1) equilibrium points for FOCGNNs. The obtained results encompass those of integer-order Hopfield neural networks with or without delay as special cases. The activation functions are nonlinear and nonmonotonic. There could be many corner points in this general class of activation functions. The structure of activation functions makes FOCGNNs could have a lot of stable equilibrium points. Coexistence of multiple stable equilibrium points is necessary when neural networks come to pattern recognition and associative memories. Finally, two numerical examples are provided to illustrate the effectiveness of the obtained results.

Key words: fractional-order Cohen-Grossberg neural networks, multiple Lagrange stability, multiple Lyapunov asymptotical stability, time delays

中图分类号: (Neural networks, fuzzy logic, artificial intelligence)

07.05.Mh

02.30.Ks (Delay and functional equations)

黄玉娇, 袁孝焰, 杨旭华, 龙海霞, 肖杰. Multiple Lagrange stability and Lyapunov asymptotical stability of delayed fractional-order Cohen-Grossberg neural networks[J]. 中国物理B, 2020, 29(2): 20703-020703.

Yu-Jiao Huang(黄玉娇), Xiao-Yan Yuan(袁孝焰), Xu-Hua Yang(杨旭华), Hai-Xia Long(龙海霞), Jie Xiao(肖杰). Multiple Lagrange stability and Lyapunov asymptotical stability of delayed fractional-order Cohen-Grossberg neural networks[J]. Chin. Phys. B, 2020, 29(2): 20703-020703.

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Multiple Lagrange stability and Lyapunov asymptotical stability of delayed fractional-order Cohen-Grossberg neural networks

Multiple Lagrange stability and Lyapunov asymptotical stability of delayed fractional-order Cohen-Grossberg neural networks

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