The Relationship Between the Multi-Collinearity and the Generalization Capability of the Neural Network Forecast Model

Abstract

With the practical problem in application of the neural network in weather forecast, the effect of the learning matrix in neural network forecast model with the multi-collinearity on the generalization capability is researched. The results show, in the context of the same input knot number, whatever the smaller network or the network getting larger, there is few changes in simulation error both the neural network models without or with multi-collinearity, and the mean simulation errors for both of the two types model are very close, but the generalization capability of the neural network with multi-collinearity is obvious superior than that without multi-collinearity. Further more, it is to analyses the generalization capability for the two types of models in different training times from 5000 to 20000, the results also indicate that the multi-collinearity have the remarkable effect on decrease the forecast precision to the neural network forecast model.