Abstract
In this paper, we propose a new method for selecting RBF centers. The strength of our method is to determine the number and the locations of RBF centers automatically without any priori assumption about the number of centers. The proposed method consists of three phases. The first phase is to partition the input patterns into the several subsets according to their output labels. In the second and third phase, the number and the locations of RBF centers are determined using bi-section algorithm and weighted mean centering. These second and third phase are iteratively repeated until to reach the goal error. The proposed method is applied to several benchmark data sets. The numerical results show that our method is robust and efficient for determining the number and the locations of centers.
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Lee, DW., Lee, J. (2004). A Novel Three-Phase Algorithm for RBF Neural Network Center Selection. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_59
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DOI: https://doi.org/10.1007/978-3-540-28647-9_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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