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.

引用

页码：718 / 727

页数：10

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