Robust stability of recurrent neural networks with ISS learning algorithm

被引：0

作者：

Choon Ki Ahn

机构：

[1] Seoul National University of Science & Technology,Department of Automotive Engineering

来源：

Nonlinear Dynamics | 2011年 / 65卷

关键词：

Input-to-state stability (ISS) approach; Weight learning algorithm; Dynamic neural networks; Linear matrix inequality (LMI);

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In this paper, an input-to-state stability (ISS) approach is used to derive a new robust weight learning algorithm for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the ISS learning algorithm is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the design of the ISS learning algorithm can be achieved by solving LMI, which can be easily facilitated by using some standard numerical packages. A numerical example is presented to demonstrate the validity of the proposed learning algorithm.

引用

页码：413 / 419

页数：6

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