Kernel LMS

In this paper a nonlinear adaptive algorithm based on a kernel space least mean squares (LMS) approach is presented. With most of the neural network based methods for time series modeling it is difficult to implement a sample-by-sample adaptation method. This puts a serious limitation on the applica...

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Bibliographic Details
Published in2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07 Vol. 3; pp. III-1421 - III-1424
Main Authors Pokharel, P. P., Weifeng Liu, Principe, J. C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2007
Subjects
Adaptive algorithm
Adaptive signal processing
Convergence
Kernel
kernel trick
Least squares approximation
LMS
Modeling
Neural networks
Nonlinear filters
Nonlinear systems
Signal processing algorithms
stochastic gradient
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ISBN9781424407279
1424407273
ISSN1520-6149
DOI10.1109/ICASSP.2007.367113

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Summary:In this paper a nonlinear adaptive algorithm based on a kernel space least mean squares (LMS) approach is presented. With most of the neural network based methods for time series modeling it is difficult to implement a sample-by-sample adaptation method. This puts a serious limitation on the applicability of adaptive nonlinear filters in many optimal signal processing and communication applications where data arrives sequentially. This paper shows that the kernel LMS algorithm provides a computational simple and an effective algorithm to train nonlinear systems for system modeling without the need for regularization, without convergence to local minima and without the need for a separate book of data as a training set.
ISBN:9781424407279
1424407273
ISSN:1520-6149
DOI:10.1109/ICASSP.2007.367113