On-line RBFNN based identification of rapidly time-varying nonlinear systems with optimal structure-adaptation

Apostolikas, G; Tzafestas, S

dc.contributor.author	Apostolikas, G	en
dc.contributor.author	Tzafestas, S	en
dc.date.accessioned	2014-03-01T01:19:23Z
dc.date.available	2014-03-01T01:19:23Z
dc.date.issued	2003	en
dc.identifier.issn	0378-4754	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15462
dc.subject	Adaptive systems modeling	en
dc.subject	Radial basis function networks	en
dc.subject	Time-varying system tracking	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.classification	Mathematics, Applied	en
dc.subject.other	Algorithms	en
dc.subject.other	Computer simulation	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Online systems	en
dc.subject.other	Time varying systems	en
dc.subject.other	Time varying system tracking	en
dc.subject.other	Radial basis function networks	en
dc.title	On-line RBFNN based identification of rapidly time-varying nonlinear systems with optimal structure-adaptation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0378-4754(02)00159-3	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0378-4754(02)00159-3	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	This paper presents an adaptive RBF network for the on-line identification and tracking of rapidly-changing time-varying nonlinear systems. The proposed algorithm is capable of maintaining the accuracy of learned patterns even when a large number of aged patterns are replaced by new ones through the adaptation process. Moreover, the algorithm exhibits a strong learning capacity with instant embodiment of new data which makes it suitable for tracking of fast-changing systems. However, the accuracy and speed in the adaptation is balanced by the computational cost which increases with the square of the number of the radial basis functions, resulting in a computational expensive, but still practically feasible, algorithm. The simulation results show the effectiveness (in terms of degradation of learned patterns and learning capacity) of this architecture for adaptive modeling. (C) 2002 Published by Elsevier Science B.V. on behalf of IMACS.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Mathematics and Computers in Simulation	en
dc.identifier.doi	10.1016/S0378-4754(02)00159-3	en
dc.identifier.isi	ISI:000182472200001	en
dc.identifier.volume	63	en
dc.identifier.issue	1	en
dc.identifier.spage	1	en
dc.identifier.epage	13	en