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Identification of nonlinear systems using generalized kernel models

Chen, S., Hong, X., Harris, C. J. and Wang, X. X. (2005) Identification of nonlinear systems using generalized kernel models. IEEE Transactions on Control Systems Technology, 13 (3). pp. 401-411. ISSN 1063-6536

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To link to this item DOI: 10.1109/tcst.2004.841652

Abstract/Summary

Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leave-one-out (LOO) test statistic and local regularization (LR) is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15172
Uncontrolled Keywords:correlation, cross validation, kernel model, leave-one-out (LOO) test, score, neural networks, nonlinear system identification, orthogonal, least squares (OLS), regression, ORTHOGONAL LEAST-SQUARES, BASIS FUNCTION NETWORKS, REGRESSION, ALGORITHM, DESIGN, SET

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