NDLI: Nonlinear System Identification based on Support Vector Machine using Particle Swarm Optimization

Content Provider	IEEE Xplore Digital Library
Author	Byung-hwa Lee Sang-un Kim Jin-wook Seok Sangchul Won
Copyright Year	2006
Description	Author affiliation: Dept. of Electron. & Electr. Eng., POSTECH, Pohang (Byung-hwa Lee; Sang-un Kim; Jin-wook Seok)
Abstract	This paper describes a different method for the identification of the nonlinear system and parameter optimization of the obtained input-output model. The approach is the technique using the least square support vector machines (LS-SVM) regression based on particle swarm optimization (PSO). LS-SVM is a regression algorithm used to approximate nonlinear function and the PSO algorithm is a optimization technique. For the nonlinear system, the system model is built by using the LS-SVM algorithm with radial basis function (RBF) kernel. Then, the hyperparameters of LS-SVM model are selected by the PSO. The analytic solution demonstrates that the PSO can be applied to optimize efficiently the hyperparameters, cost weighting factor and RBF-kernel width, used in the LS-SVM model. So, the reliability of the formulated model is improved as compared to that of the direct LS-SVM model, which have the non-optimized parameters, and can obtain the optimum parameters rapidly. In addition, based on the obtained nonlinear LS-SVM model, the numerical simulation results in pulp washing process and CSTR system illustrate the effectiveness and the merits of this algorithm
Starting Page	5614
Ending Page	5618
File Size	5736209
Page Count	5
File Format	PDF
ISBN	8995003847
DOI	10.1109/SICE.2006.315099
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-18
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	The Institute of Control, Automation and Systems
Subject Keyword	Nonlinear systems Support vector machines Particle swarm optimization Optimization methods Least squares methods Least squares approximation Kernel Cost function Numerical models Numerical simulation CSTR system Least squares support vector machines particle swarm optimization RBF-kernel pulp washing process
Content Type	Text
Resource Type	Article

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