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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Chang, PC., Liu, CH., Yeh, CH., Chen, SH. (2006). The Development of a Weighted Evolving Fuzzy Neural Network. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence. ICIC 2006. Lecture Notes in Computer Science(), vol 4114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37275-2_28
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DOI: https://doi.org/10.1007/978-3-540-37275-2_28
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37274-5
Online ISBN: 978-3-540-37275-2
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