Identification of weakly nonlinear systems based on Support Vector Machines

Author

Marconato, Anna ; Boni, Andrea ; Petri, Dario ; Schoukens, Johan

Author_Institution

Dept. of Inf. & Commun. Technol., Univ. of Trento, Trento, Italy

fYear

2009

fDate

5-7 May 2009

Firstpage

122

Lastpage

127

Abstract

In this work we analyze the application of Support Vector Machines for Regression (SVRs) to the problem of identifying weakly nonlinear systems. Examples of simple linear and nonlinear systems are considered, taking into account both non-recursive and recursive models. When defining the SVR estimating function, several kinds of kernels are employed, and the effect on the accuracy performance of reducing the training set size is studied.

Keywords

identification; learning (artificial intelligence); nonlinear systems; recursive functions; regression analysis; support vector machines; SVR estimating function; nonrecursive model; recursive model; support vector machine; training set size reduction; weak nonlinear system identification; Artificial neural networks; Communications technology; Information analysis; Instrumentation and measurement; Kernel; Least squares approximation; Nonlinear systems; Performance evaluation; Support vector machines; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 2009. I2MTC '09. IEEE

Conference_Location

Singapore

ISSN

1091-5281

Print_ISBN

978-1-4244-3352-0

Type

conf

DOI

10.1109/IMTC.2009.5168428

Filename

5168428