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.
Relation:
Lecture Notes in Artificial Intelligence 4114, p.212-221
