Abstract
A novel learning algorithm is presented to construct radial basis function (RBF) networks by incorporating partial least squares (PLS) regression method. The algorithm selects hidden units one by one with PLS regression method until an adequate network is achieved, and the resulting minimal RBF-PLS (MRBF-PLS) network exhibits satisfying generalization performance and noise toleration capability. The algorithm provides an efficient approach for system identification, and this is illustrated by modelling nonlinear function and chaotic time series.
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© 2008 Springer-Verlag Berlin Heidelberg
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Wang, N., Liu, X., Yin, J. (2008). A Hybrid Model of Partial Least Squares and RBF Neural Networks for System Identification. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_23
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DOI: https://doi.org/10.1007/978-3-540-87732-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
Online ISBN: 978-3-540-87732-5
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