Intelligent initialization of resource allocating RBF networks

Wallace, M; Tsapatsoulis, N; Kollias, S

dc.contributor.author	Wallace, M	en
dc.contributor.author	Tsapatsoulis, N	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T01:22:31Z
dc.date.available	2014-03-01T01:22:31Z
dc.date.issued	2005	en
dc.identifier.issn	0893-6080	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16602
dc.subject	Ionosphere	en
dc.subject	Resource allocating networks	en
dc.subject	Wisconsin	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Data reduction	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Resource allocation	en
dc.subject.other	Clustering methods	en
dc.subject.other	Clustering processes	en
dc.subject.other	Network structures	en
dc.subject.other	Resource allocating networks (RAN)	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	architecture	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	cluster analysis	en
dc.subject.other	intelligence	en
dc.subject.other	mathematical model	en
dc.subject.other	parameter	en
dc.subject.other	priority journal	en
dc.subject.other	reaction time	en
dc.subject.other	resource allocation	en
dc.subject.other	response time	en
dc.subject.other	Algorithms	en
dc.subject.other	Artificial Intelligence	en
dc.subject.other	Bayes Theorem	en
dc.subject.other	Computer Simulation	en
dc.subject.other	Humans	en
dc.subject.other	Intelligence	en
dc.subject.other	Neural Networks (Computer)	en
dc.title	Intelligent initialization of resource allocating RBF networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.neunet.2004.11.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.neunet.2004.11.005	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	In any neural network system, proper parameter initialization reduces training time and effort, and generally leads to compact modeling of the process under examination, i.e. less complex network structures and better generalization. However, in cases of multi-dimensional data, parameter initialization is both difficult and time consuming. In the proposed scheme a novel, multi-dimensional, unsupervised clustering method is used to properly initialize neural network architectures, focusing on resource allocating networks (RAN); both the hidden and output layer parameters are determined by the output of the clustering process, without the need for any user interference. The main contribution of this work is that the proposed approach leads to network structures that are compact, efficient and achieve best classification results, without the need for manual selection of suitable initial network parameters. The efficiency of the proposed method has been tested on several classes of publicly available data, such as iris, Wisconsin and ionosphere data. (c) 2005 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Neural Networks	en
dc.identifier.doi	10.1016/j.neunet.2004.11.005	en
dc.identifier.isi	ISI:000228440100002	en
dc.identifier.volume	18	en
dc.identifier.issue	2	en
dc.identifier.spage	117	en
dc.identifier.epage	122	en