Abstract
A new model selection algorithm is established to determine the best number of hidden neurons for radial basis function neural networks. We used a Bayesian information-based criterion to select the best number of hidden neurons. The new algorithm grows the number of hidden neurons while the Bayesian information-based criterion is used for improvement. The optimal parameter values of a current neural network are used in the subsequent architecture. The computational results are compared with the trial-and-error approach through publicly available data sets. It is found that the new algorithm is suitable to improve the performance of the neural networks automatically. The root mean square error function is used to measure out-of-sample performance.
Highlights
Radial Basis Function Neural Networks (RBFNNs) (Broomhead and Lowe, 1988; Lee et al, 2011; Zhao et al, 2014; Wang et al, 2016) have elicited a lot of attention in the past
We proposed a new algorithm to select automatically the most suitable RBFNN topology for classification
We proposed a new method to select the best RBFNN architecture
Summary
Radial Basis Function Neural Networks (RBFNNs) (Broomhead and Lowe, 1988; Lee et al, 2011; Zhao et al, 2014; Wang et al, 2016) have elicited a lot of attention in the past. We determined the parameters' values of the neural networks by using the Levenberg-Marquardt Algorithm (LMA) (Moré, 1978; Ye, 2003; Heertjes and Verstappen, 2014). The first step determines the basis function parameters cj and σj based on the X-values of the training samples.
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