Using support vector machines for stability region determination

Author

Zhang, Z.H. ; Ong, C.J. ; Keerthi, S.S. ; Gilbert, E.G.

Author_Institution

Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore

Volume

2

fYear

2002

fDate

18-22 Nov. 2002

Firstpage

915

Abstract

The paper presents a new approach to determine the stability region for constrained dynamical systems. Our approach employs support vector machines (SVMs), a promising new tool for pattern recognition, to this field. By this application, the determination of stability region becomes a typical two-class hard margin pattern recognition problem, rather than the characterizations of the boundaries of such stability regions. In the underlying analysis, a program has been developed to generate critical points in the state space and train them by SVMs. Some examples are given to show the obtained estimates are close approximations of the exact stability region.

Keywords

computational complexity; control system analysis computing; nonlinear dynamical systems; pattern recognition; stability; support vector machines; SVMs; constrained dynamical systems; critical points; pattern recognition; stability region determination; state space; support vector machines; Asymptotic stability; Character recognition; Control systems; Jacobian matrices; Mechanical engineering; Pattern recognition; Power system stability; State-space methods; Support vector machines; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on

Print_ISBN

981-04-7524-1

Type

conf

DOI

10.1109/ICONIP.2002.1198194

Filename

1198194