Stabilization of a generalized stochastic neural network with distributed parameters

Author

Luo, Qi ; Deng, Fei-qi ; Jun-Dong Bao

Author_Institution

Inst. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou, China

Volume

2

fYear

2003

fDate

2-5 Nov. 2003

Firstpage

1231

Abstract

In this paper, we study stabilization of Cohen-Grossberg type generalized neural networks with distributed parameters. The main idea is to treat the integral of the stochastic field solution to the system as the solution process to the corresponding stochastic ordinary differential equation and then discuss stabilization of the process of solution. The integral is with respect to the spatial variables. To implement this idea, we use the Itoˆ differential formula to compute the differential of a Lyapunov function along the system. The Lyapunov function is the average respect to the spatial variables. We found that there is no Itoˆ formula for the stochastic systems with distributed parameters, up to now. Our method overcomes this difficulty. Also, we have not seen any result on the stochastic neural networks with distributed parameters.

Keywords

Lyapunov methods; distributed parameter systems; neural nets; partial differential equations; stability; stochastic processes; Cohen-Grossberg type generalized neural networks; Lyapunov function; differential equation; distributed parameters; generalized stochastic neural network; partial differential system; spatial variables; stabilization; Artificial neural networks; Biological system modeling; Biological systems; Circuit stability; Lyapunov method; Mathematics; Neural networks; Partial differential equations; Stochastic processes; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2003 International Conference on

Print_ISBN

0-7803-8131-9

Type

conf

DOI

10.1109/ICMLC.2003.1259675

Filename

1259675