A state estimator of stochastic delayed neural networks

Author

Zhang, Chunxiao ; Chen, Yun ; Wang, Junhong

Author_Institution

Sch. of Autom., Hangzhou Dianzi Univ., Hangzhou, China

fYear

2012

fDate

23-25 May 2012

Firstpage

2829

Lastpage

2832

Abstract

The problem of state estimation for stochastic Hopfield neural networks with time-varying delay is investigated in this paper. Based on an auxiliary vector and free-weighting matrix technique, a delay-dependent Luenbergertype state estimator, which ensures mean-square asymptotic stability of the resulting filtering error state system, is designed. In this paper, the model transformations and cross terms bounding techniques are avoided. A numerical example is proposed to show the validity of the method.

Keywords

Hopfield neural nets; asymptotic stability; delays; filtering theory; matrix algebra; mean square error methods; state estimation; stochastic processes; time-varying systems; auxiliary vector matrix technique; cross term bounding techniques; delay-dependent Luenberger-type state estimator; filtering error state system; free-weighting matrix technique; mean-square asymptotic stability; model transformations; state estimation; stochastic Hopfield neural networks; stochastic delayed neural networks; time-varying delay; Biological neural networks; Delay; Stability analysis; State estimation; Stochastic processes; Vectors; Lyapunov-Krasovskii functional; Stochastic neural networks; auxiliary vector; time-varying delay;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2012 24th Chinese

Conference_Location

Taiyuan

Print_ISBN

978-1-4577-2073-4

Type

conf

DOI

10.1109/CCDC.2012.6243063

Filename

6243063