Title :
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
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;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 2009. I2MTC '09. IEEE
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-3352-0
DOI :
10.1109/IMTC.2009.5168428
