Title :
A fuzzy neural hybrid system modeling
Author :
Türksen, I. Burhan
Author_Institution :
Dept. of Mech.-Ind. Eng., Toronto Univ., Ont., Canada
Abstract :
In this paper, we propose and discuss a fuzzy-neural system development schema. For this purpose, we identify three knowledge representation and approximate reasoning approaches. For the Type I fuzzy theory, we describe the extraction of fuzzy sets and fuzzy rules with the application of an improved fuzzy clustering technique which is essentially an unsupervised learning of the fuzzy sets and rules from a given input-output data set. Next we describe how this set of rules and their fuzzy sets may be adapted and/or modified for known target sets with supervised learning within a fuzzified neural network architecture. Finally, we introduce a unified (fuzzy) approximate reasoning formulation for fuzzy modeling and control
Keywords :
expert systems; fuzzy control; fuzzy neural nets; fuzzy set theory; fuzzy systems; inference mechanisms; knowledge representation; learning (artificial intelligence); modelling; uncertainty handling; approximate reasoning; fuzzy clustering; fuzzy control; fuzzy expert systems; fuzzy neural network; fuzzy rules; fuzzy set theory; fuzzy-neural system; knowledge representation; modeling; supervised learning; unsupervised learning; Data mining; Fuzzy control; Fuzzy reasoning; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Knowledge representation; Modeling; Supervised learning; Unsupervised learning;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.614428
