The Open Mechanical Engineering Journal

2015, 9 : 1062-1066
Published online 2015 October 28. DOI: 10.2174/1874155X01509011062
Publisher ID: TOMEJ-9-1062

Research on the Prediction Model of Material Cost Based on Data Mining

Liu Shenyang , Gao Qi , Li Zhen , Li Si and Li Zhiwei
Department of Equipment Command & Management of Mechanical Engineering College, Shijiazhuang, Hebei, 050003, P.R. China.

ABSTRACT

Material cost prediction should be based on the scientific mathematical models so that the influence of subjective factors on the quota and other indicators of decomposition can be reduced. This paper analyzes the particle swarm optimization (PSO) algorithm to optimize the parameters of support vector machine and establishes the prediction model of material cost after preprocessing the actual data and uses the support vector regression (SVR) machine to carry out data mining. In the forecasting process, the total cost of material is first predicted and the predicted results are then adjusted with the actual value, and finally, the relative errors are tested. The result indicates that the forecasting effect is fulfilled.

Keywords:

Data mining, optimization algorithm, particle swarm prediction of material cost, support vector machine.