Abstract
To improve the on-line predictive capability of radial basis function (RBF) networks, a novel sequential learning algorithm is developed referred to as sequential orthogonal model selection (SOMS) algorithm. The RBF network is adapted on-line for both network structure and connecting parameters. Based on SOMS algorithm, a multi-step predictive control strategy is introduced and applied to ship control. Simulation results of ship course control experiment demonstrate the applicability and effectiveness of the SOMS algorithm.
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Wang, N., Liu, X., Yin, J. (2008). An On-Line Learning Radial Basis Function Network and Its Application. 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_22
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DOI: https://doi.org/10.1007/978-3-540-87732-5_22
Publisher Name: Springer, Berlin, Heidelberg
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