DocumentCode
1905910
Title
A neuro-fuzzy approach with pairwise comparisons
Author
Ichihashi, H. ; Turksen, I.B.
Author_Institution
Dept. of Ind. Eng., Osaka Prefecture Univ., Japan
fYear
1993
fDate
1993
Firstpage
1010
Abstract
An iterative learning algorithm in fuzzy models, which is called neuro-fuzzy, has been developed within the framework of fuzzy modeling. Using the neuro-fuzzy approach, two quantification methods of pairwise comparisons are presented in order to derive the associated weights of different objects. The proposed methods can be applied even in the case of incomplete pairwise comparisons. A simplified fuzzy reasoning model is obtained in the form of Gaussian radial basis functions
Keywords
fuzzy set theory; inference mechanisms; iterative methods; learning (artificial intelligence); neural nets; uncertainty handling; Gaussian radial basis functions; fuzzy models; fuzzy reasoning; iterative learning; neuro-fuzzy; pairwise comparisons; quantification methods; Artificial neural networks; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Humans; Industrial engineering; Input variables; Iterative algorithms; Psychology;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993., IEEE International Conference on
Conference_Location
San Francisco, CA
Print_ISBN
0-7803-0999-5
Type
conf
DOI
10.1109/ICNN.1993.298696
Filename
298696
Link To Document