Robust passivity analysis for discrete-time recurrent neural networks with mixed delays

Chuan Kuei Huang, Yu Jeng Shu, Koan Yuh Chang, Ho Nien Shou, Chien Yu Lu

研究成果: Article

1 引文 斯高帕斯(Scopus)

摘要

This article considers the robust passivity analysis for a class of discrete-time recurrent neural networks (DRNNs) with mixed time-delays and uncertain parameters. The mixed time-delays that consist of both the discrete time-varying and distributed time-delays in a given range are presented, and the uncertain parameters are norm-bounded. The activation functions are assumed to be globally Lipschitz continuous. Based on new bounding technique and appropriate type of Lyapunov functional, a sufficient condition is investigated to guarantee the existence of the desired robust passivity condition for the DRNNs, which can be derived in terms of a family of linear matrix inequality (LMI). Some free-weighting matrices are introduced to reduce the conservatism of the criterion by using the bounding technique. A numerical example is given to illustrate the effectiveness and applicability.

原文English
頁(從 - 到)216-232
頁數17
期刊International Journal of Electronics
102
發行號2
DOIs
出版狀態Published - 2015 二月 1

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

指紋 深入研究「Robust passivity analysis for discrete-time recurrent neural networks with mixed delays」主題。共同形成了獨特的指紋。

  • 引用此