An improved novelty criterion for resource allocating networks


Online model order complexity estimation remains one of the key problems in neural network research. The problem is further exacerbated in situations where the underlying system generator is non-stationary. In this paper, we introduce a novelty criterion for resource allocating networks (RANs) which is capable of being applied to both stationary and slowly varying non-stationary problems. The deficiencies of existing novelty criteria are discussed and the relative performances are demonstrated on two real-world problems : electricity load forecasting and exchange rate prediction.

Divisions: Aston University (General)
Additional Information: ©1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Event Title: Fifth International Conference on Artificial Neural Networks
Event Type: Other
Event Dates: 1997-07-07 - 1997-07-07
Uncontrolled Keywords: feedforward neural nets,electricity load forecasting,exchange rate prediction,extended Kalman filter training,algorithm,network growth,network growth prescription,nonstationary real-world problems,novelty criterion,radial basis function network resource allocation,signal processing theory,slowly varying nonstationary environment
ISBN: 0852966903
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://ieeexplo ... c?punumber=4811 (Publisher URL)
PURE Output Type: Chapter
Published Date: 1997-07-07
Authors: McLachlan, Alan



Version: Published Version

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