A review on evolution of Lyapunov-Krasovskii function in stability analysis of recurrent neural networks with single time-varying delay

Author

Zhanshan Wang ; Zhengwei Shen ; Mi Tian ; Qihe Shan

Author_Institution

State Key Lab. of Synthetical Autom. for Process Ind., Northeastern Univ., Shenyang, China

fYear

2014

fDate

6-11 July 2014

Firstpage

2471

Lastpage

2476

Abstract

In the stability analysis of recurrent neural networks, one of the tasks is to reduce the conservativeness of the stability criterion. Along this routine, there are two ways to be considered. One is how to construct the Lyapunov-Krasovskii functional (LKF), and the other is how to use mathematical skills to estimate the derivatives of the LKF. The purpose of this paper is to present a brief review on the evolution on the construction of LKF for recurrent neural networks with single time-varying delay. By summarizing the observation, one can find the core elements in the construction of LKF. Moreover, one can find the evolution history on the delay-partitioning and its applications in the construction of LKF.

Keywords

Lyapunov methods; delays; recurrent neural nets; stability; Lyapunov-Krasovskii functional evolution; delay-partitioning; recurrent neural networks; single time-varying delay; stability analysis; Asymptotic stability; Delay effects; Delays; Recurrent neural networks; Stability criteria;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), 2014 International Joint Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-6627-1

Type

conf

DOI

10.1109/IJCNN.2014.6889531

Filename

6889531