Title :
A neuro-fuzzy approach with pairwise comparisons
Author :
Ichihashi, H. ; Turksen, I.B.
Author_Institution :
Dept. of Ind. Eng., Osaka Prefecture Univ., Japan
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;
Conference_Titel :
Neural Networks, 1993., IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0999-5
DOI :
10.1109/ICNN.1993.298696
