An adaptive resource allocating neuro-fuzzy inference system with sensitivity analysis resource control

Pertselakis, M; Raouzaiou, N; Stafylopatis, A

dc.contributor.author	Pertselakis, M	en
dc.contributor.author	Raouzaiou, N	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T01:29:49Z
dc.date.available	2014-03-01T01:29:49Z
dc.date.issued	2009	en
dc.identifier.issn	15715736	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19355
dc.subject	Adaptive Behavior	en
dc.subject	Curse of Dimensionality	en
dc.subject	Dynamic System	en
dc.subject	Intelligent System	en
dc.subject	Resource Allocation	en
dc.subject	Resource Control	en
dc.subject	Sensitivity Analysis	en
dc.subject	Structure Learning	en
dc.subject	neuro fuzzy inference system	en
dc.title	An adaptive resource allocating neuro-fuzzy inference system with sensitivity analysis resource control	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/978-1-4419-0221-4_59	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-1-4419-0221-4_59	en
heal.publicationDate	2009	en
heal.abstract	Adaptability in non-stationary contexts is a very important property and a constant desire for modern intelligent systems and is usually associated with dynamic system behaviors. In this framework, we present a novel methodology of dynamic resource control and optimization for neurofuzzy inference systems. Our approach involves a neurofuzzy model with structural learning capabilities that adds rule nodes when necessary during the training phase. Sensitivity analysis is then applied to the trained network so as to evaluate the network rules and control their usage in a dynamic manner based on a confidence threshold. Therefore, on one hand, we result in a well-balanced structure with an improved adaptive behavior and, on the other hand, we propose a way to control and restrict the ""curse of dimensionality"". The experimental results on a number of classification problems prove clearly the strengths and benefits of this approach. © 2009 International Federation for Information Processing.	en
heal.journalName	IFIP International Federation for Information Processing	en
dc.identifier.doi	10.1007/978-1-4419-0221-4_59	en
dc.identifier.volume	296	en
dc.identifier.spage	509	en
dc.identifier.epage	516	en