A neural-network approach to modeling and analysis

Author

Chen, Chen-Yuan ; Chen, Cheng-Wu ; Chiang, Wei-Ling ; Hwang, Jing-Dong

Author_Institution

Dept. of Marine Environ. & Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan

fYear

2002

fDate

2002

Firstpage

489

Lastpage

493

Abstract

A backpropagation network can always be used in modeling. This study is concerned with the stability problem of a neural network (NN) system which consists of a few subsystems represented by NN models. In this paper, the dynamics of each NN model is converted into linear inclusion representation. Subsequently, based on the representations, the stability conditions in terms of Lyapunov´s direct method is derived to guarantee the asymptotic stability of NN systems.

Keywords

Lyapunov methods; asymptotic stability; backpropagation; digital simulation; neural nets; stability criteria; Lyapunov direct method; NN model dynamics; asymptotic stability; backpropagation network; linear inclusion representation; modeling; neural network system stability; neural-network approach; stability; subsystems; Aerodynamics; Asymptotic stability; Biological neural networks; Biological system modeling; Interconnected systems; Large-scale systems; Neural networks; Nonlinear control systems; Stability analysis; Stability criteria;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. 14th IEEE International Conference on

ISSN

1082-3409

Print_ISBN

0-7695-1849-4

Type

conf

DOI

10.1109/TAI.2002.1180843

Filename

1180843