A New Method for Constructing Radial Basis Function Neural Networks

Ignoring the samples far away from the training samples, our study team gives a new norm-based derivative process of localized generalization error boundary. Enlightened by the above research, this paper proposes a new method to construct radial basis function neural networks, which minimizes the sum of training error and stochastic sensitivity. Experimental results show that the new method can lead to simple and better network architecture