EXPONENTIAL STABILITY AND TRAJECTORY BOUNDS OF NEURAL NETWORKS UNDER STRUCTURAL VARIATIONS

被引：21

作者：

GRUJIC, LT ^{[1
]}

MICHEL, AN ^{[1
]}

机构：

[1] UNIV NOTRE DAME, DEPT ELECT & COMP ENGN, NOTRE DAME, IN 46556 USA

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS | 1991年 / 38卷 / 10期

关键词：

D O I：

10.1109/31.97538

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The dynamic behavior of neural networks under arbitrary unknown structural perturbations depends essentially on the compatibility/incompatibility of input variables in these networks. Estimates of the upper bounds of the motions of neural networks of either type and exponential stability of compatible neural networks are established by using three different forms of Lyapunov functions. Moreover, conditions for the maximum possible estimate of the domain of structural exponential stability are determined. All new concepts such as compatible/incompatible neural networks and structural exponential stability are defined. The obtained results are in the form suitable for straightforward applications.

引用

页码：1182 / 1192

页数：11

共 17 条

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