Title :
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
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;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1198194
