Time series prediction using evolving radial basis function networks with new encoding scheme

Du, H; Zhang, N

Time series prediction using evolving radial basis function networks with new encoding scheme

Du, H

Zhang, N

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2008, 71 (7-9), pp. 1388 - 1400
Issue Date:: 2008-03-01

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Field	Value	Language
dc.contributor.author	Du, H https://orcid.org/0000-0002-3439-3821	en_US
dc.contributor.author	Zhang, N https://orcid.org/0000-0001-6408-0044	en_US
dc.date.issued	2008-03-01	en_US
dc.identifier.citation	Neurocomputing, 2008, 71 (7-9), pp. 1388 - 1400	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10430
dc.description.abstract	This paper presents a new encoding scheme for training radial basis function (RBF) networks by genetic algorithms (GAs). In general, it is very difficult to select the proper input variables and the exact number of nodes before training an RBF network. In the proposed encoding scheme, both the architecture (numbers and selections of nodes and inputs) and the parameters (centres and widths) of the RBF networks are represented in one chromosome and evolved simultaneously by GAs so that the selection of nodes and inputs can be achieved automatically. The performance and effectiveness of the presented approach are evaluated using two benchmark time series prediction examples and one practical application example, and are then compared with other existing methods. It is shown by the simulation tests that the developed evolving RBF networks are able to predict the time series accurately with the automatically selected nodes and inputs. © 2007 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2007.06.004	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Time series prediction using evolving radial basis function networks with new encoding scheme	en_US
dc.type	Journal Article
utslib.citation.volume	7-9	en_US
utslib.citation.volume	71	en_US
utslib.for	170205 Neurocognitive Patterns and Neural Networks	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
dc.location.activity	ISI:000255239200025	en_US
dc.location.activity	Louis Laybourne Smith School of Architecture and Design, University of South Australia
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access
pubs.issue	7-9	en_US
pubs.publication-status	Published	en_US
pubs.volume	71	en_US

Abstract:

This paper presents a new encoding scheme for training radial basis function (RBF) networks by genetic algorithms (GAs). In general, it is very difficult to select the proper input variables and the exact number of nodes before training an RBF network. In the proposed encoding scheme, both the architecture (numbers and selections of nodes and inputs) and the parameters (centres and widths) of the RBF networks are represented in one chromosome and evolved simultaneously by GAs so that the selection of nodes and inputs can be achieved automatically. The performance and effectiveness of the presented approach are evaluated using two benchmark time series prediction examples and one practical application example, and are then compared with other existing methods. It is shown by the simulation tests that the developed evolving RBF networks are able to predict the time series accurately with the automatically selected nodes and inputs. © 2007 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/10430