Radial basis function neural networks for adaptive on-line identification of rapidly time-varying nonlinear systems with optimal adaptation to new structures

Apostolikas, G; Tzafestas, S

dc.contributor.author	Apostolikas, G	en
dc.contributor.author	Tzafestas, S	en
dc.date.accessioned	2014-03-01T02:42:09Z
dc.date.available	2014-03-01T02:42:09Z
dc.date.issued	2002	en
dc.identifier.issn	08843627	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30817
dc.subject	Adaptive	en
dc.subject	Identification	en
dc.subject	Radial Basis Function Networks	en
dc.subject	Time-Varying System	en
dc.subject	Tracking	en
dc.subject.other	Adaptive systems	en
dc.subject.other	Algorithms	en
dc.subject.other	Computer simulation	en
dc.subject.other	Learning systems	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Time varying networks	en
dc.subject.other	Time-varying nonlinear systems	en
dc.subject.other	Radial basis function networks	en
dc.title	Radial basis function neural networks for adaptive on-line identification of rapidly time-varying nonlinear systems with optimal adaptation to new structures	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICSMC.2002.1176369	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICSMC.2002.1176369	en
heal.publicationDate	2002	en
heal.abstract	This paper presents an adaptive RBF network for the on-line identification and tracking of rapidly changing time-varying non-linear 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.	en
heal.journalName	Proceedings of the IEEE International Conference on Systems, Man and Cybernetics	en
dc.identifier.doi	10.1109/ICSMC.2002.1176369	en
dc.identifier.volume	5	en
dc.identifier.spage	289	en
dc.identifier.epage	294	en