A Variable-Weight Neural Network Combined Predicting Model to the Trend Predicting of the State Development of the Large-scale Rotary Sets

摘要

In the trend prediction to the state development of the large-scale rotary sets, the most important problem is how to enhance the accuracy of prediction. The combined predicting model based on neural networks can realize a comprehensive utilization to the effective information offered by several prediction methods and receive an optimum prediction result. But in the traditional combined predicting model, the weight coefficients are calculated complicatedly. And the current combined predicting model based on neural networks lacks enough emphases to the advantages of different prediction methods. Therefore, a new combined predicting model based on variable-weight neural networks is presented in this paper. And the weight coefficients of all methods in the new combined predicting model are also determined. By using the new model, a satisfying predictive effect is received in the application of the large-scale rotary sets.