In order to predict the wearing of stellite alloys, the related methods of rare metals data processing were discussed. The method of opposite degree （OD） algorithm was put forward to predict the wearing of stellite ...In order to predict the wearing of stellite alloys, the related methods of rare metals data processing were discussed. The method of opposite degree （OD） algorithm was put forward to predict the wearing of stellite alloys. OD algorithm is based on prior numerical data, posterior numerical data and the opposite degree between numerical forecast data. To compare the performance of predicted results based on different algorithms, the back propagation （BP） and radial basis function （RBF） neural network methods were introduced. Predicted results show that the relative error of OD algorithm is smaller than those of BP and RBF neural network methods. OD algorithm is an effective method to predict the wearing of stellite alloys and it can be applied in practice.展开更多
基金financially supported by the National Natural Science Foundation of China (Nos. 51374164, 51174153, 51104111 and 51104112)the Self-Determined and Innovative Research Funds of Wuhan University of Technology (No.2014-JL-007)the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20120143110005)
文摘In order to predict the wearing of stellite alloys, the related methods of rare metals data processing were discussed. The method of opposite degree （OD） algorithm was put forward to predict the wearing of stellite alloys. OD algorithm is based on prior numerical data, posterior numerical data and the opposite degree between numerical forecast data. To compare the performance of predicted results based on different algorithms, the back propagation （BP） and radial basis function （RBF） neural network methods were introduced. Predicted results show that the relative error of OD algorithm is smaller than those of BP and RBF neural network methods. OD algorithm is an effective method to predict the wearing of stellite alloys and it can be applied in practice.
