The multistability of delayed competitive neural networks with piecewise non-monotonic activation functions

被引：2
|
作者：
Zhang, Yan [1 ]
Qiao, Yuanhua [1 ]
Duan, Lijuan [2 ]
Miao, Jun [3 ]
机构：
[1] Beijing Univ Technol, Fac Sci, Beijing 100124, Peoples R China
[2] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
[3] Beijing Informat Sci & Technol Univ, Sch Comp Sci, Beijing 100101, Peoples R China
来源：
MATHEMATICAL METHODS IN THE APPLIED SCIENCES | 2022年 / 45卷 / 16期
基金：
中国国家自然科学基金;
关键词：
competitive neural networks; multistability; non-monotonic piecewise nonlinear activation functions; time-varying delays; GLOBAL EXPONENTIAL STABILITY; ASSOCIATIVE MEMORY; INSTABILITY; CAPACITY;
D O I：
10.1002/mma.8368
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper addresses the problem of multistability of competitive neural networks with nonlinear, non-monotonic piecewise activation functions and time-varying delays. Several sufficient conditions are proposed to guarantee the existence of (2K+1)(n) equilibrium points and the locally exponential stability of (K+1)(n) equilibrium points, where K is a positive integer and determined by the property of activation functions and the parameters of neural networks. The quantitative relationship between the equilibrium points of the system and the zero roots of the bounding functions is given. In addition, the attraction basins of the exponentially stable equilibrium points are obtained. Finally, a numerical simulation is given to illustrate the effectiveness of the obtained results.
引用
收藏
页码：10295 / 10311
页数：17
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