Sparse generalized kernel modeling for nonlinear systems

S. Chen*, X. Hong, X. X. Wang, C. J. Harris

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

A generalized kernel modeling approach is proposed for identification of discrete-time nonlinear systems. Each kernel regresser in the generalized kernel model has an individually fitted diagonal covariance matrix which is determined by maximizing the correlation between the regresser and training data. A state-of-the-art construction algorithm based on orthogonal least squares regression with leave-one-out test statistic and local regularization is applied to select a parsimonious generalized kernel model from the full regression matrix. The effectiveness of the proposed nonlinear modeling approach is demonstrated by the experimental results involving one simulated system and two real data sets.

Original languageEnglish
Title of host publicationProceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05
Pages2574-2579
Number of pages6
Volume2005
DOIs
Publication statusPublished - 1 Dec 2005
Event44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05 - Seville, United Kingdom
Duration: 12 Dec 200515 Dec 2005

Conference

Conference44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05
CountryUnited Kingdom
CitySeville
Period12/12/0515/12/05

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    Chen, S., Hong, X., Wang, X. X., & Harris, C. J. (2005). Sparse generalized kernel modeling for nonlinear systems. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05 (Vol. 2005, pp. 2574-2579). [1582550] https://doi.org/10.1109/CDC.2005.1582550