The development of a weighted evolving fuzzy neural network

Authors:
Pei-Chann Chang;Chen-Hao Liu;Chia-Hsuan Yeh;Shih-Hsin Chen
Affiliations:
Department of Industrial Engineering and Management, Yuan-Ze University, Taoyuan, Taiwan, R.O.C.;Department of Industrial Engineering and Management, Yuan-Ze University, Taoyuan, Taiwan, R.O.C.;Department of Information Management, Yuan-Ze University, Taoyuan, Taiwan, R.O.C.;Department of Industrial Engineering and Management, Yuan-Ze University, Taoyuan, Taiwan, R.O.C.
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

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Abstract

This study modifies the Evolving Fuzzy Neural Network Framework (EFuNN framework) proposed by Kasabov (1998) and adopts a weighted factor to calculate the importance of each factor among these different rules. In addition, an exponential transfer function (exp (-D)) is employed to transfer the distance of any two factors into the value of similarity among different rules, thus a different rule clustering method is developed accordingly. The intensive experimental results show that the WEFuNN performs very well when applied in the PCB sales forecasting.