Exponential Stability of Matrix-Valued BAM Neural Networks with Time-Varying Delays

被引：0
|
作者：
Popa, Calin-Adrian [1 ]
机构：
[1] Polytech Univ Timisoara, Dept Comp & Software Engn, Blvd V Parvan 2, Timisoara 300223, Romania
来源：
NEURAL INFORMATION PROCESSING (ICONIP 2017), PT III | 2017年 / 10636卷
关键词：
Matrix-valued BAM neural networks; Exponential stability; Linear matrix inequality; Time-varying delays;
D O I：
10.1007/978-3-319-70090-8_72
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Matrix-valued BAM neural networks are a generalization of real-valued BAM neural networks, for which the states, weights, and outputs are square matrices. This paper gives a sufficient criterion expressed in terms of linear matrix inequalities, for which the equilibrium point of these networks with time-varying delays is exponentially stable. A numerical example is provided to demonstrate the effectiveness of the proposed criterion.
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页码：718 / 727
页数：10
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