NDLI: A dynamic regularized radial basis function network for nonlinear, nonstationary time series prediction

Content Provider	IEEE Xplore Digital Library
Author	Yee, P. Haykin, S.
Copyright Year	1991
Abstract	In this paper, constructive approximation theorems are given which show that under certain conditions, the standard Nadaraya-Watson (1964) regression estimate (NWRE) can be considered a specially regularized form of radial basis function networks (RBFNs). From this and another related result, we deduce that regularized RBFNs are m.s., consistent, like the NWRE for the one-step-ahead prediction of Markovian nonstationary, nonlinear autoregressive time series generated by an i.i.d. noise processes. Additionally, choosing the regularization parameter to be asymptotically optimal gives regularized RBFNs the advantage of asymptotically realizing minimum m.s. prediction error. Two update algorithms (one with augmented networks/infinite memory and the other with fixed-size networks/finite memory) are then proposed to deal with nonstationarity induced by time-varying regression functions. For the latter algorithm, tests on several phonetically balanced male and female speech samples show an average 2.2-dB improvement in the predicted signal/noise (error) ratio over corresponding adaptive linear predictors using the exponentially-weighted RLS algorithm. Further RLS filtering of the predictions from an ensemble of three such RBFNs combined with the usual autoregressive inputs increases the improvement to 4.2 dB, on average, over the linear predictors.
Sponsorship	IEEE Signal Processing Society
Starting Page	2503
Ending Page	2521
Page Count	19
File Size	554875
File Format	PDF
ISSN	1053587X
Volume Number	47
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radial basis function networks Resonance light scattering Neural networks Signal processing algorithms Predictive models Noise generators Testing Speech enhancement Signal to noise ratio Nonlinear filters
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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