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�������պ����ѧѧ�� 2010, Vol. 36 Issue (10) :1189-1193    DOI:
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Structure selection and parameter set-membership identification for nonlinear systems
He Liqing, Sun Xianfang, Qiu Hongzhuan*
School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

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Abstract�� Based on support vector regression and radial basis function (RBF) neural network, the set-membership identification for nonlinear systems with unknown-but-bounded noise was investigated. The relationships among the ���-insensitive parameter, noise bounds and the number of support vectors were deduced, and the method of determining the ���-insensitive parameter using the noise bounds was introduced. The algorithm of choosing the scale of RBF neural network via support vector regression was described, in which the Gaussian kernel function was taken as the radial basis function and the support vector as its center parameters. After the structure of the RBF neural network was determined, all the feasible weight vectors of the RBF neural network were found by the optimal bounding ellipsoid (OBE) algorithm and a class of feasible nonlinear models were formed which were consistent with the given noise bound series and the input-output data set. A simulation example shows that the proposed algorithm is effective.
Keywords�� nonlinear systems   unknown-but-bounded noise   parameter estimation   set-membership identification��   radial basis function (RBF) neural network     
Received 2009-08-31; published 2010-11-12


About author: ������(1979-),Ů,ɽ����ͬ��,��ʿ��,Heliqing@asee.buaa.edu.cn.
������, ���ȷ�, ���ר.������ϵͳ�Ľṹѡ��������ļ�Ա��ʶ[J]  �������պ����ѧѧ��, 2010,V36(10): 1189-1193
He Liqing, Sun Xianfang, Qiu Hongzhuan.Structure selection and parameter set-membership identification for nonlinear systems[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2010,V36(10): 1189-1193
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