NDLI: Identification of Nonlinear Systems Using Multi-scale Wavelet Support Vectors Machines

Content Provider	IEEE Xplore Digital Library
Author	Lei Yang Jiuqiang Han Dake Chen
Copyright Year	2007
Description	Author affiliation: Xi'an Jiaotong Univ., Xi'an (Lei Yang; Jiuqiang Han; Dake Chen)
Abstract	New identification method of non-linear dynamic systems based on multi-scale wavelet least squares support vector machines (MS-LS-SVM) is proposed. Support vector machines (SVM) is a novel machine learning method based on small-sample statistical learning theory, which is powerful to deal with small sample, nonlinearity, high dimension, and local minima. Least squares support vector machines (LS-SVM) is an updating SVM version which involve equality instead of inequality constraints of standard SVM to simplify the process of SVM. Wavelet function with different resolution is used as kernel function in order to construct MS-LS-SVM. The condition of support vector kernel function is proved. This kind of kernel function can simulate almost any function in quadratic integral space, so it enhances the generalization ability of the SVM. According to the multi-scale wavelet kernel function and regularization theory, MS-LS-SVM regression model is proposed. The regression model formulates a new identification method of non-linear systems. Experiments show the proposed method not only has better identification precision, but also improves robustness and generalization than neural networks.
Starting Page	1779
Ending Page	1784
File Size	395105
Page Count	6
File Format	PDF
ISBN	9781424408177
DOI	10.1109/ICCA.2007.4376667
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-05-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nonlinear systems Support vector machines Kernel Neural networks Support vector machine classification Least squares methods Pattern recognition Power system modeling Nonlinear control systems Control systems regression models Identification of Nonlinear Systems multi-scale wavelet least squares support vector machines wavelet kernel function
Content Type	Text
Resource Type	Article

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