Adaptable neural networks for modeling recursive non-linear systems

Doulamis, N; Doulamis, A; Varvarigou, T

dc.contributor.author	Doulamis, N	en
dc.contributor.author	Doulamis, A	en
dc.contributor.author	Varvarigou, T	en
dc.date.accessioned	2014-03-01T02:49:07Z
dc.date.available	2014-03-01T02:49:07Z
dc.date.issued	2002	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34326
dc.subject	Adaptive Neural Network	en
dc.subject	Dynamic Adaptation	en
dc.subject	Efficient Algorithm	en
dc.subject	linear functionals	en
dc.subject	Non Linear System	en
dc.subject	Recursive Estimation	en
dc.subject	Satisfiability	en
dc.subject	Taylor Series	en
dc.subject	Traffic Prediction	en
dc.subject	First Order	en
dc.title	Adaptable neural networks for modeling recursive non-linear systems	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICDSP.2002.1028306	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICDSP.2002.1028306	en
heal.publicationDate	2002	en
heal.abstract	An efficient algorithm for recursive estimation of a non-linear autoregression (NAR) model is proposed. In particular, the model parameters are dynamically adapted through time so that (a) the model response, after the parameter updating, satisfies the current conditions and (b) a minimal modification of the model parameters is accomplished. The first condition is expressed by applying a first-order Taylor series	en
heal.journalName	International Conference on Digital Signal Processing	en
dc.identifier.doi	10.1109/ICDSP.2002.1028306	en