Title :
A fuzzy neural network with trapezoid fuzzy weights
Author :
Ishibuchi, Hisao ; Morioka, Kouichi ; Tanaka, Hideo
Author_Institution :
Dept. of Ind. Eng., Osaka Prefectural Univ., Osaka, Japan
Abstract :
Proposes a fuzzy neural network architecture whose weights are given as trapezoid fuzzy numbers. The proposed fuzzy neural network can handle fuzzy inputs as well as real inputs. In both cases, outputs from the fuzzy neural network are fuzzy numbers. Next, we derive a learning algorithm from a cost function defined for level sets (i.e. α-cuts) of fuzzy outputs and fuzzy targets. Lastly, we examine the ability of the proposed fuzzy neural network to implement fuzzy IF-THEN rules by computer simulation
Keywords :
fuzzy neural nets; learning (artificial intelligence); neural net architecture; virtual machines; α-cuts; computer simulation; cost function; fuzzy IF-THEN rules; fuzzy inputs; fuzzy neural network architecture; fuzzy numbers; fuzzy outputs; fuzzy targets; learning algorithm; level sets; real inputs; trapezoid fuzzy weights; Computer architecture; Computer simulation; Cost function; Fuzzy neural networks; Fuzzy sets; Industrial engineering; Level set; Neural networks;
Conference_Titel :
Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1896-X
DOI :
10.1109/FUZZY.1994.343681
