Hopf Bifurcation Analysis of a Delayed Fractional BAM Neural Network Model with Incommensurate Orders

被引：6

作者：

Li, Bingbing ^{[1
]}

Liao, Maoxin ^{[1
]}

Xu, Changjin ^{[2
]}

Li, Weinan ^{[1
]}

机构：

[1] Univ South China, Sch Math & Phys, Hengyang 421001, Hunan, Peoples R China

[2] Guizhou Univ Finance & Econ, Guizhou Key Lab Econ Syst Simulat, Guiyang 550004, Guizhou, Peoples R China

来源：

NEURAL PROCESSING LETTERS | 2023年 / 55卷 / 05期

基金：

湖南省自然科学基金; 中国国家自然科学基金;

关键词：

Fractional-order BAM neural networks; Hopf bifurcation; Stability; Incommensurate orders; STABILITY ANALYSIS; EXISTENCE; SYSTEM;

D O I：

10.1007/s11063-022-11118-8

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, a six-neuron incommensurate fractional order BAM neural network model with multi-delays is considered. We demonstrate that the equilibrium point of the system loses its stability and Hopf bifurcation emerges when the delay passes through a critical value. And the relationship between the critical delay of Hopf bifurcation and size of fractional orders is found. Finally, some numerical simulations are given to verify the validity of the theoretical results.

引用

页码：5905 / 5921

页数：17

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