An improved neural network for fuzzy reasoning implementation

Tzafestas, SG; Stamou, GB

dc.contributor.author	Tzafestas, SG	en
dc.contributor.author	Stamou, GB	en
dc.date.accessioned	2014-03-01T01:11:40Z
dc.date.available	2014-03-01T01:11:40Z
dc.date.issued	1996	en
dc.identifier.issn	0378-4754	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/11774
dc.subject	connectionist models	en
dc.subject	Decision Making Process	en
dc.subject	Fuzzy Reasoning	en
dc.subject	Fuzzy Set	en
dc.subject	Fuzzy System	en
dc.subject	Human Performance	en
dc.subject	neuro-fuzzy system	en
dc.subject	Expert System	en
dc.subject	neuro fuzzy inference system	en
dc.subject	Neural Net	en
dc.subject	Neural Network	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	Computer architecture	en
dc.subject.other	Computer simulation	en
dc.subject.other	Decision making	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Inference engines	en
dc.subject.other	Learning systems	en
dc.subject.other	Parallel processing systems	en
dc.subject.other	Theorem proving	en
dc.subject.other	Collective system	en
dc.subject.other	Connectionist models	en
dc.subject.other	Fuzzy reasoning	en
dc.subject.other	Hamacher fuzzy intersection function	en
dc.subject.other	Human performance	en
dc.subject.other	Neuro-fuzzy inference system	en
dc.subject.other	Parallel interconnections	en
dc.subject.other	Sugeno complement function	en
dc.subject.other	Trapezoidal fuzzy sets	en
dc.subject.other	Neural networks	en
dc.title	An improved neural network for fuzzy reasoning implementation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/0378-4754(95)00007-0	en
heal.identifier.secondary	http://dx.doi.org/10.1016/0378-4754(95)00007-0	en
heal.language	English	en
heal.publicationDate	1996	en
heal.abstract	Neural networks or connectionist models are massively parallel interconnections of simple neurons that work as a collective system, can emulate human performance and provide high computation rates. On the other hand, fuzzy systems are capable to model uncertain or ambiguous situations that are so often encountered in real life. One way for implementing fuzzy systems is through utilizations of the expert system architecture. Recently, many attempts have been made to ""fuse"" fuzzy systems and neural nets in order to achieve better performance in reasoning and decision making processes. The systems that result from such a fusion are called neuro-fuzzy inference systems and possess combined features. The purpose of the present paper is to propose such a neuro-fuzzy system by extending and improving the system of Keller et al. (1992). The present system makes use of Hamacher's fuzzy intersection function and Sugeno's complement function. After a brief outline of the operation of the system its features are established with the aid of four theorems which are fully proved. The capabilities of the system are shown by a set of simulation results derived for the case of trapezoidal fuzzy sets. These results are shown to be better than the ones obtained with the original neuro-fuzzy system of Keller et al.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Mathematics and Computers in Simulation	en
dc.identifier.doi	10.1016/0378-4754(95)00007-0	en
dc.identifier.isi	ISI:A1996UR67100006	en
dc.identifier.volume	40	en
dc.identifier.issue	5-6 SPEC. ISS.	en
dc.identifier.spage	565	en
dc.identifier.epage	576	en