Abstract
Support vector machine (SVM) resembles RBF neural networks in structure. Considering their resemblance, a new optimization algorithm based on support vector machine and genetic algorithm for RBF neural network is presented, in which GA is used to choose the SVM model parameter and SVM is used to help constructing the RBF. The network based on this algorithm is applied on nonlinear system identification. Simulation results show that the network based on this algorithm has higher precision and better generalization ability.
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© 2012 Springer-Verlag Berlin Heidelberg
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Nong, J. (2012). Radial Basis Function Neural Networks Optimization Algorithm Based on SVM. In: Zhao, M., Sha, J. (eds) Communications and Information Processing. Communications in Computer and Information Science, vol 289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31968-6_35
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DOI: https://doi.org/10.1007/978-3-642-31968-6_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31967-9
Online ISBN: 978-3-642-31968-6
eBook Packages: Computer ScienceComputer Science (R0)