Abstract
In this paper, we describe a novel approach to fuzzy clustering which organizes the data in clusters on the basis of the input data and builds a ’prototype’ regression function as a summation of linear local regression models to guide the clustering process. This methodology is shown to be effective in the training of RBFNN’s. It is shown that the performance of such networks is better than other types of networks.
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© 2006 Springer-Verlag Berlin Heidelberg
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Staiano, A., Tagliaferri, R., Pedrycz, W. (2006). Linear Regression Model-Guided Clustering for Training RBF Networks for Regression Problems. In: Di Gesú, V., Masulli, F., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2003. Lecture Notes in Computer Science(), vol 2955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10983652_17
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DOI: https://doi.org/10.1007/10983652_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31019-8
Online ISBN: 978-3-540-32683-0
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