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

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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 http://hdl.handle.net/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


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