Abstract
Based on the principle of Radial Basis Function (RBF) Neural Network, a learning method is presented for the identification of a complex system model. The RBF algorithm is employed on the learning and identifying process of the nonlinear model. The simulation results show that the presented method has good effect on speeding up the learning and approaching process of the nonlinear complex model, and has an excellent performance on learning convergence.
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© 2004 Springer-Verlag Berlin Heidelberg
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Song, Y., Wang, P., Li, K. (2004). Complex Model Identification Based on RBF Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_35
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DOI: https://doi.org/10.1007/978-3-540-28648-6_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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