An improved novelty criterion for resource allocating networks

Alan McLachlan

An improved novelty criterion for resource allocating networks

Alan McLachlan

Research output: Chapter in Book/Published conference output › Chapter

Abstract

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.

Original language	English
Title of host publication	Fifth International Conference on Artificial Neural Networks
Publisher	IEEE
Pages	48-52
Number of pages	5
Volume	440
ISBN (Print)	0852966903
Publication status	Published - 7 Jul 1997
Event	Fifth International Conference on Artificial Neural Networks - Duration: 7 Jul 1997 → 7 Jul 1997

Publication series

Name	Conference publication
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Volume	440

Conference

Conference	Fifth International Conference on Artificial Neural Networks
Period	7/07/97 → 7/07/97

Bibliographical note

©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.

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

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An improved novelty criterion for resource allocating networks
©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.
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Cite this

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title = "An improved novelty criterion for resource allocating networks",

abstract = "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.",

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AB - 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.

KW - feedforward neural nets

KW - electricity load forecasting

KW - exchange rate prediction

KW - extended Kalman filter training

KW - algorithm

KW - network growth

KW - network growth prescription

KW - nonstationary real-world problems

KW - novelty criterion

KW - radial basis function network resource allocation

KW - signal processing theory

KW - slowly varying nonstationary environment

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UR - http://ieeexplore.ieee.org/servlet/opac?punumber=4811

M3 - Chapter

SN - 0852966903

VL - 440

T3 - Conference publication

SP - 48

EP - 52

BT - Fifth International Conference on Artificial Neural Networks

PB - IEEE

T2 - Fifth International Conference on Artificial Neural Networks

Y2 - 7 July 1997 through 7 July 1997

ER -

An improved novelty criterion for resource allocating networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

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