Coexistence and Local Exponential Stability of Multiple Equilibria in Memristive Neural Networks with a Class of General Nonmonotonic Activation Functions

被引：0
|
作者：
Huang, Yujiao [1 ]
Chen, Shijun [1 ]
Xiao, Jie [1 ]
Hao, Pengyi [1 ]
机构：
[1] Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou 310023, Zhejiang, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS, PT I | 2017年 / 10261卷
基金：
中国国家自然科学基金;
关键词：
Memristive neural network; Coexistence; Local exponential stability; General nonmonotonic activation function; SYNCHRONIZATION; MULTISTABILITY;
D O I：
10.1007/978-3-319-59072-1_42
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 addresses the multistability problem of n-dimensional memristive neural networks with a class of general nonmonotonic activation functions. Sufficient conditions are proposed for checking the existence of (2l + 3)(n) equilibria, of which (l + 2)(n) equilibria are locally exponentially stable. The obtained stability results improve and extend the existing ones. One numerical example is given to illustrate the effectiveness of the obtained results.
引用
收藏
页码：354 / 362
页数：9
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