A new constructive neural network method for noise processing and its application on stock market prediction ?
Some noise data are considered as singular values of a continuous function. RBF neural networks are constructed to fit the singular value with every ? error. A theorem about a function with m jumping discontinuity points has been proved. The constructive part has no generalization influence to the learning system. A real world problem has been presented to verify the correctness of the theory.
noise stock market prediction neural network architecture decay rbf neural networks overfitting