UM
Kernel normalized mixed-norm algorithm for system identification
Shujian Yu1; Xinge You2; Kexin Zhao1; Weihua Ou3; Yuanyan Tang2,4
2015-09-28
Conference Name2015 International Joint Conference on Neural Networks (IJCNN)
Source PublicationProceedings of the International Joint Conference on Neural Networks
Volume2015-September
Conference Date12-17 July 2015
Conference PlaceKillarney, Ireland
CountryIreland
Abstract

Kernel methods provide an efficient nonparametric model to produce adaptive nonlinear filtering (ANF) algorithms. However, in practical applications, standard squared error based kernel methods suffer from two main issues: (1) a constant step size is used, which degrades the algorithm performance in non-stationary environment, and (2) additive noises are assumed to follow Gaussian distribution, while in practice the noises are generally non-Gaussian and follow other statistical distributions. To address these two issues simultaneously, this paper proposes a novel kernel normalized mixed-norm (KNMN) algorithm. Compared to the standard squared error based kernel methods, the KNMN algorithm extends the linear mixed-norm adaptive filtering algorithms to Reproducing Kernel Hilbert Space (RKHS) and introduces a normalized step size as well as adaptive mixing parameter. We also conduct the mean square convergence analysis and demonstrate the desirable performance of the KNMN algorithm in solving the system identification problem.

KeywordAdaptation Models Noise
DOIhttps://doi.org/10.1109/IJCNN.2015.7280588
URLView the original
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic
WOS IDWOS:000370730602023 ISBN:978-1-4799-1959-8
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA
2.Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA
3.School of Mathematics and Computer Science, Guizhou Normal University, Guiyang, Guizhou, China
4.Faculty of Science and Technology, University of Macau, Macau, China
Recommended Citation
GB/T 7714
Shujian Yu,Xinge You,Kexin Zhao,et al. Kernel normalized mixed-norm algorithm for system identification[C],2015.
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