A new constructive neural network method for noise processing and its application on stock market prediction

In this paper, in order to optimize neural network architecture and generalization, after analyzing the reasons of overfitting and poor generalization of the neural networks, we presented a class of constructive decay RBF neural networks to repair the singular value of a continuous function with finite number of jumping discontinuity points. We proved that a function with m jumping discontinuity points can be approximated by a simplest neural network and a decay RBF neural network in L-2(R) by each epsilon error, and a function with m jumping discontinuity point y = f(x), x is an element of E subset of R-d can be constructively approximated by a decay RBF neural network in L-2(R-d) by each epsilon > 0 error. Then the whole networks will have less hidden neurons and well generalization in the same of the first part. A real world problem about stock closing price with jumping discontinuity have been presented and verified the correctness of the theory. (C) 2013 Elsevier B.V. All rights reserved.