NDLI: Nonlinear system modeling based on KPCA and MKSVM

Content Provider	IEEE Xplore Digital Library
Author	Zhiyong Du Xianfang Wang Liyuan Zheng Zhulin Zheng
Copyright Year	2009
Description	Author affiliation: Henan Mechanical and Electrical Engineering College Xinxiang, P.R. China 453002 (Zhiyong Du) \|\| Henan Institute of Science and Technology Xinxiang, P.R. China 453003 (Xianfang Wang; Zhulin Zheng) \|\| School of electrical and control Engineering Liaoning Technical University, Liaoning Huludao 125105 (Liyuan Zheng)
Abstract	Kernels are employed in Support Vector Machines (SVM) to map the nonlinear model into a higher dimensional feature space where the linear learning is adopted. Every kernel has its advantages and disadvantages. Preferably, the ‘good’ characteristics of two or more kernels should be combined. In this paper, the mathematical formulation of multiple kernel learning is given. To enhance the robust regression of the algorithm, KPCA is used for the support vectors' reduced process. Through the implementation for average molecular weight in polyacrylonitrile productive process, it demonstrates the good performance of the proposed method compared to single kernel.
Starting Page	61
Ending Page	64
File Size	398372
Page Count	4
File Format	PDF
ISBN	9781424442478
DOI	10.1109/CCCM.2009.5268039
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	KPCA Communication system control Nonlinear control systems nonlinear system model kernel function Lagrangian functions Support vector machines Control engineering support vector machine Engineering management Machine learning Risk management Nonlinear systems Kernel
Content Type	Text
Resource Type	Article

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