NEURAL NETWORK NONLINEAR REGRESSION MODELING AND INFORMATION CRITERIA

We consider the problem of constructing nonlinear regression models, using multilayer perceptrons and radial basis function network with the help of the technique of regularization. Crucial issues in the model building process are the choices of the number of basis functions, the number of hidden units and a regularization parameter. We consider the properties of nonlinear regression modeling based on neural networks, and investigate the performance of model selection criteria from an information-theoretic point of view.