Title :
Residue Amending Combined Prediction Model Based on RBF Neural Network
Author :
Kong Li-Fang ; Zhang Hong ; Wang Zhe
Author_Institution :
Sch. of Inf. & Electr. Eng., China Univ. of Min. & Technol., Xuzhou, China
Abstract :
The thesis introduces grey system model and RBF neural network. In the light of the drawbacks and merits of the two models, the author puts forward the residue amending combined prediction model, and makes a contrast between the three models in prediction and precision. The result indicates that, the combined model is better than that of the single models for higher precision and smaller error.
Keywords :
engines; grey systems; lubricating oils; mechanical engineering computing; radial basis function networks; wear; RBF neural network; engine lubricating oil analysis; grey system model; residue amending combined prediction model; wear metal analysis; Differential equations; Electronic mail; Engines; Linear regression; Lubricating oils; Machinery; Neural networks; Petroleum; Predictive models; Statistics;
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
DOI :
10.1109/CISE.2009.5364035
