Generalized H2 State estimation of stochastic neural networks with time-varying delay

Author

Mingang Hua ; Jianyong Zhang ; Juntao Fei

Author_Institution

Coll. of Comput. & Inf., Hohai Univ., Changzhou, China

fYear

2014

fDate

9-12 Sept. 2014

Firstpage

1153

Lastpage

1158

Abstract

The generalized H₂ state estimation problem for stochastic neural networks is considered int this paper. The delay-dependent design criterion is established such that the error system is asymptotical mean-square stable with a prescribed generalized H₂ performance. The gain matrix and the optimal performance index are obtained by solving a generalized eigenvalue problem. Numerical example is given to show the effectiveness of the proposed design methods.

Keywords

H^∞ control; asymptotic stability; delay systems; eigenvalues and eigenfunctions; matrix algebra; neurocontrollers; stochastic systems; asymptotic stability; delay-dependent design; gain matrix; generalized H₂ state estimation; generalized eigenvalue problem; mean-square stability; optimal performance index; stochastic neural network; time-varying delay; Biological neural networks; Delays; Estimation error; Neurons; State estimation; Trajectory; Asymptotical mean-square stability; Generalized H2 state estimation; Linear matrix inequalities; Stochastic neural networks; Time-varying delay;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE Annual Conference (SICE), 2014 Proceedings of the

Conference_Location

Sapporo

Type

conf

DOI

10.1109/SICE.2014.6935256

Filename

6935256