Identification of chaotic process systems with least squares support vector machines

Author

Jemwa, G.T. ; Aldrich, C.

Author_Institution

Dept. of Chem. Eng., Stellenbosch Univ., South Africa

Volume

3

fYear

2003

fDate

20-24 July 2003

Firstpage

2066

Abstract

We investigate the nonlinear identification of chaotic process systems with least squares support vector machines, based on a case study of a parallel cubic autocatalytic reaction. State space reconstruction techniques are used to obtain a low-dimensional representation of the system in a different, but equivalent coordinate system. The performance of the support vector machine models are compared to corresponding models obtained when using multilayer perceptron neural networks, which are known to model chaotic dynamical systems well.

Keywords

catalysis; chaos; identification; least squares approximations; multilayer perceptrons; nonlinear dynamical systems; support vector machines; chaotic dynamical systems; chaotic process systems; coordinate system; least squares support vector machines; multilayer perceptron neural networks; nonlinear identification; parallel cubic autocatalytic reaction; state space reconstruction techniques; Africa; Chaos; Chemical engineering; Delay effects; Least squares methods; Multilayer perceptrons; Neural networks; Risk management; State-space methods; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223726

Filename

1223726