NDLI: Feedforward control based on the particle filter realization of SVM

Content Provider	IEEE Xplore Digital Library
Author	Zonghai Sun Xuhua Yang Youxian Sun
Copyright Year	2004
Description	Author affiliation: Nat. Lab. of Ind.Control Technol., Zhejiang Univ., Hangzhou, China (Zonghai Sun; Xuhua Yang; Youxian Sun)
Abstract	Support vector machine is a new and promising technique for pattern classification and regression estimation. The training of support vector machine is characterized by convex optimization problem, up to the determination of a few additional tuning parameters. Moreover, the model complexity follows from this convex optimization problem. In this paper we introduce sequential support vector machine for regression estimation. The support vector machine is trained by the particle filter. By the method of training support vector machine we can design the controller based on support vector machine. Afterwards we discuss feedforward control based on support vector machine. Support vector machine builds the inverse model of plant in the feedforward control based on support vector machine. In order to demonstrate the availability of this new controller designing method, we give a simulation of the simple nonlinear system for output tracking problem. The results of simulation demonstrate this method of new controller designing is very effective.
Starting Page	3547
Ending Page	3548
File Size	281908
Page Count	2
File Format	PDF
ISBN	0780385667
ISSN	1062922X
DOI	10.1109/ICSMC.2004.1400890
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-10
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle filters Support vector machines Support vector machine classification Covariance matrix Sun Quadratic programming State estimation Parameter estimation Taylor series Signal processing algorithms
Content Type	Text
Resource Type	Article

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