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A new algorithm for online structure and parameter adaptation of RBF networks

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dc.contributor.author Alexandridis, A en
dc.contributor.author Sarimveis, H en
dc.contributor.author Bafas, G en
dc.date.accessioned 2014-03-01T01:18:32Z
dc.date.available 2014-03-01T01:18:32Z
dc.date.issued 2003 en
dc.identifier.issn 0893-6080 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/15075
dc.subject Adaptive modeling en
dc.subject Dynamic modeling en
dc.subject Dynamic systems en
dc.subject Fuzzy clustering en
dc.subject Radial basis functions en
dc.subject Structure adaptation en
dc.subject Time varying systems en
dc.subject Training methods en
dc.subject.classification Computer Science, Artificial Intelligence en
dc.subject.other Algorithms en
dc.subject.other Extrapolation en
dc.subject.other Fuzzy sets en
dc.subject.other Problem solving en
dc.subject.other Radial basis function networks en
dc.subject.other Fuzzy subspaces en
dc.subject.other Neural networks en
dc.subject.other adaptation en
dc.subject.other algorithm en
dc.subject.other article en
dc.subject.other artificial neural network en
dc.subject.other dynamics en
dc.subject.other online system en
dc.subject.other priority journal en
dc.subject.other Adaptation, Biological en
dc.subject.other Algorithms en
dc.subject.other Neural Networks (Computer) en
dc.title A new algorithm for online structure and parameter adaptation of RBF networks en
heal.type journalArticle en
heal.identifier.primary 10.1016/S0893-6080(03)00052-2 en
heal.identifier.secondary http://dx.doi.org/10.1016/S0893-6080(03)00052-2 en
heal.language English en
heal.publicationDate 2003 en
heal.abstract This paper deals with the problem of online adaptation of radial basis function (RBF) neural networks. A new adaptive training method is presented. which is able to modify both the structure of the network (the number of nodes in the hidden layer) and the output weights, as the algorithm proceeds. These adaptation capabilities make the algorithm suitable for modeling dynamical time varying systems, where not only the dynamics but also the operating region changes with time. Therefore, the important issue of extrapolation is faced successfully, but at the same time the algorithm takes care of the size of the network, by deleting the hidden node centers that remain inactive for a long time. The selection of the network centers is based on a fuzzy partition of the input space, which defines a number of fuzzy subspaces. The algorithm considers the centers of the fuzzy subspaces as candidates for becoming hidden node centers and makes the selections, so that at least one center is close enough to each input example. The proposed technique is illustrated through the application to time varying dynamical systems and is compared to other adaptive training methods. (C) 2003 Elsevier Science Ltd. All rights reserved. en
heal.publisher PERGAMON-ELSEVIER SCIENCE LTD en
heal.journalName Neural Networks en
dc.identifier.doi 10.1016/S0893-6080(03)00052-2 en
dc.identifier.isi ISI:000185078700006 en
dc.identifier.volume 16 en
dc.identifier.issue 7 en
dc.identifier.spage 1003 en
dc.identifier.epage 1017 en


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