A Novel Recursive Algorithm for Training RBF Networks
A recursive learning algorithm is presented for basis selection of radial basis function (RBF) neural network. It is based on an adaptive kernel width algorithm, which can select basis functions recursively in the nonorthogonal space and assign an appropriate number of hidden units of RBF network. This also makes the model structure independent of the selected term sequence and assures an optimal RBF network even if the RBF original basis is nonorthogonal. Its effectiveness is demonstrated by the simulated results.
KeywordsRadial basis function Bayesian information criterion Recursive
- 5.Deng J, Li K, Irwin GW (2010) A two-stage algorithm for automatic construction of RBF neural model. In: 15th IEEE mediterranean electrotechnical conference, vol 5795-9. pp 166–171Google Scholar
- 8.Peng Z, Dehua L, Hong W, Feng C (2011) The automatic model selection and variable width for RBF neural networks. Neurocomputing 74:3628–3637Google Scholar