Abstract
Evolutionary multiobjective optimization approach to RBF networks structure determination is discussed in this chapter. The candidates of RBF network structure are encoded into the chromosomes in GAs and they evolve toward the Pareto optimal front defined by the several objective functions with regard to model accuracy and model complexity. Then, an ensemble of networks is constructed by using the Pareto optimal networks. We discuss its application to nonlinear system identification. Numerical simulation results indicate that the ensemble network is much more robust for the case of existence of outliers or lack of data, than the one selected based on information criteria.
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Hatanaka, T., Kondo, N., Uosaki, K. (2006). Multi-Objective Structure Selection for RBF Networks and Its Application to Nonlinear System Identification. In: Jin, Y. (eds) Multi-Objective Machine Learning. Studies in Computational Intelligence, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33019-4_21
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DOI: https://doi.org/10.1007/3-540-33019-4_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30676-4
Online ISBN: 978-3-540-33019-6
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