Global Exponential Stability for Matrix-Valued Neural Networks with Time Delay

被引：1

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：

ADVANCES IN NEURAL NETWORKS, PT I | 2017年 / 10261卷

关键词：

Matrix-valued neural networks; Global stability; Linear matrix inequality; Time delay; MULTILAYER PERCEPTRONS; VARYING DELAYS; NEURONS;

D O I：

10.1007/978-3-319-59072-1_51

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Complex-, quaternion-, and Clifford-valued neural networks can all be generalized to matrix-valued neural networks, which have matrix states. This paper derives a sufficient criterion given in the form of linear matrix inequalities that guarantees the global exponential stability of the equilibrium point for matrix-valued Hopfield neural networks with time delay. A simulation example demonstrates the effectiveness of the theoretical results.

引用

页码：429 / 438

页数：10

共 23 条

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