NDLI: Gradient radial basis function networks for nonlinear and nonstationary time series prediction

Content Provider	IEEE Xplore Digital Library
Author	Chng, E.S. Chen, S. Mulgrew, B.
Copyright Year	1990
Abstract	We present a method of modifying the structure of radial basis function (RBF) network to work with nonstationary series that exhibit homogeneous nonstationary behavior. In the original RBF network, the hidden node's function is to sense the trajectory of the time series and to respond when there is a strong correlation between the input pattern and the hidden node's center. This type of response, however, is highly sensitive to changes in the level and trend of the time series. To counter these effects, the hidden node's function is modified to one which detects and reacts to the gradient of the series. We call this new network the gradient RBF (GRBF) model. Single and multistep predictive performance for the Mackey-Glass chaotic time series were evaluated using the classical RBF and GRBF models. The simulation results for the series without and with a tine-varying mean confirm the superior performance of the GRBF predictor over the RBF predictor.
Sponsorship	IEEE Computational Intelligence Society
Starting Page	190
Ending Page	194
Page Count	5
File Size	486485
File Format	PDF
ISSN	10459227
Volume Number	7
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radial basis function networks Predictive models Chaos Counting circuits Vectors Laboratories Solid modeling Least squares methods
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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