Global Exponential Stability of Impulsive Fuzzy High-Order BAM Neural Networks With Continuously Distributed Delays

被引：38

作者：

Yang, Wengui ^{[1
]}

Yu, Wenwu ^{[1
,2
]}

Cao, Jinde ^{[1
]}

机构：

[1] Southeast Univ, Sch Math, Nanjing 210096, Jiangsu, Peoples R China

[2] King Abdulaziz Univ, Fac Engn, Jeddah 21589, Saudi Arabia

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2018年 / 29卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Fuzzy neural networks; global exponential stability; high-order bidirectional associative memory (BAM) neural networks; impulses; periodic solutions; TIME-VARYING DELAYS; BIDIRECTIONAL ASSOCIATIVE MEMORIES; PERIODIC-SOLUTION; ASYMPTOTIC STABILITY; HOPF-BIFURCATION; EXISTENCE; CRITERIA; SCALES; COEFFICIENTS; TERMS;

D O I：

10.1109/TNNLS.2017.2736581

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper investigates the stability of equilibrium point and periodic solution for impulsive fuzzy high-order bidirectional associative memory neural networks with continuously distributed delays. By applying the inequality analysis technique, M-matrix, and Banach contraction mapping principle and constructing some suitable Lyapunov functionals, some sufficient conditions for the uniqueness and global exponential stability of equilibrium point and global exponential stability of periodic solutions are established. In addition, three examples with numerical simulations are presented to demonstrate the feasibility and effectiveness of the theoretical results.

引用

页码：3682 / 3700

页数：19

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