Application of Hybrid Neural Network in Predicting Thermal Error of Ball Screw
Improving the machining accuracy of CNC machine tools is conducive to reducing the influence of thermal positioning error.This paper establishes a variable weight thermal error prediction method for a mixed model of Radial Basis Function Neu-ral Network(RBFNN)and Time-Series(ARIMA).The thermal error of the CNC machine tool is predicted by integrating two single models,the optimal weight of the two models is obtained by using the reverse identification optimization algorithm,and obtain the variable weight hybrid model,so that the prediction accuracy of thermal error is improved.To verify the feasibility of the model in this paper,the proposed model in this paper and the RBFNN model,and the ARIMA model are experimentally veri-fied and compared.The results show that the prediction accuracy of the mixed model(RBFNN-ARIMA)is significantly better than the single RBFNNand single ARIMAmodels,which proves the effectiveness of the proposed algorithm.
Radial Basis Function Neural NetworkTime SeriesVariable WeightsThermal ErrorInverse Iden-tification
NETL
NSTL
万方数据
维普
