Abstract
Radial Basis Function (RBF) networks are widely applied in function approximation, system identification, chaotic time series forecasting, etc. To use a RBF network, a training algorithm is absolutely necessary for determining the network parameters. The existing training algorithms, such as Orthogonal Least Squares (OLS) algorithm, clustering and gradient descent algorithm, have their own shortcomings. In this paper, we make an attempt to explore the applicability of Quantum-behaved Particle Swarm Optimization, a newly proposed evolutionary search technique, in training RBF neural network. The proposed QPSO-Trained RBF network was test on nonlinear system identification problem, and the results show that it can identifying the system more quickly and precisely than that trained by Particle Swarm algorithm.
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Sun, J., Xu, W., Liu, J. (2006). Training RBF Neural Network Via Quantum-Behaved Particle Swarm Optimization. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_126
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DOI: https://doi.org/10.1007/11893257_126
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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