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USING LEAST SQUARE SVM FOR NONLINEAR SYSTEMS MODELING AND CONTROL
Author(s) -
Zhang Haoran,
Wang Xiaodong,
Zhang Changjiang,
Xu Xiuling
Publication year - 2007
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1111/j.1934-6093.2007.tb00326.x
Subject(s) - structural risk minimization , support vector machine , generalization , nonlinear system , artificial neural network , least squares support vector machine , minification , computer science , matrix (chemical analysis) , artificial intelligence , control theory (sociology) , algorithm , control (management) , mathematical optimization , machine learning , mathematics , mathematical analysis , physics , materials science , quantum mechanics , composite material
Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a class of regression method with good generalization ability. The paper firstly introduces the mathematical model of regression least squares support vector machine (LSSVM), and designs incremental learning algorithms by the calculation formula of block matrix, then uses LSSVM to model nonlinear system, based on which to control nonlinear systems by model predictive method. Simulation experiments indicate that the proposed method provides satisfactory performance, and it achieves superior modeling performance to the conventional method based on neural networks, moreover it achieves well control performance.