A flexible neurofuzzy cell structure for general fuzzy inference

Tzafestas, S; Raptis, S; Stamou, G

dc.contributor.author	Tzafestas, S	en
dc.contributor.author	Raptis, S	en
dc.contributor.author	Stamou, G	en
dc.date.accessioned	2014-03-01T01:11:35Z
dc.date.available	2014-03-01T01:11:35Z
dc.date.issued	1996	en
dc.identifier.issn	0378-4754	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/11725
dc.subject	Decision Making	en
dc.subject	Fuzzy Inference	en
dc.subject	Fuzzy Rules	en
dc.subject	Knowledge Base	en
dc.subject	Multi Input Multi Output	en
dc.subject	Multi Input Single Output	en
dc.subject	Neural Network	en
dc.subject	Single Input Single Output	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	Approximation theory	en
dc.subject.other	Computer simulation	en
dc.subject.other	Decision theory	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Inference engines	en
dc.subject.other	Knowledge based systems	en
dc.subject.other	Probability	en
dc.subject.other	Flexible inference cells	en
dc.subject.other	Multi input multi output	en
dc.subject.other	Multi input single output	en
dc.subject.other	Single input single output	en
dc.subject.other	Neural networks	en
dc.title	A flexible neurofuzzy cell structure for general fuzzy inference	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/0378-4754(95)00072-0	en
heal.identifier.secondary	http://dx.doi.org/10.1016/0378-4754(95)00072-0	en
heal.language	English	en
heal.publicationDate	1996	en
heal.abstract	This paper presents and investigates a neural network structure which can perform general fuzzy inference. This system consists of a number of parallel neural network units which are called ""flexible inference cells"" (FICs). Each FIC implements a single-input/single-output (SISO) IF-THEN rule of a fuzzy knowledge base. The assumption of SISO fuzzy rules allows the implementation of any complex fuzzy inference algorithm (for control or other decision making purposes), since any MIMO (multi-input/multi-output) rule can be decomposed into an equivalent set of MISO (multi-input/single-output) rules, and a MISO rule can be decomposed to a set of SISO rules. The paper discusses the assumptions and requirements for the proposed neurofuzzy structure, and classifies the FICs into four categories. Some results derived by simulation using 3125 exemplar patterns produced computationally are provided.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Mathematics and Computers in Simulation	en
dc.identifier.doi	10.1016/0378-4754(95)00072-0	en
dc.identifier.isi	ISI:A1996UW30700004	en
dc.identifier.volume	41	en
dc.identifier.issue	3-4	en
dc.identifier.spage	219	en
dc.identifier.epage	233	en