Prediction of Agricultural Machinery Total Power Based on PSO-GM(2,1, λ, ρ) Model

In order to improve the prediction accuracy of agricultural machinery total power then to provide the basis for the agricultural mechanization development goals, the paper used gray GM(2,1) model in the prediction. Through the introduction of parameter λ to correct the background value and parameter p for multiple transformation on the initial data, the model was expanded to GM (2,1, λ, ρ) model and prediction accuracy was improved. Because of the nonlinear traits between parameter X,p and the prediction errors, they are dif-ficult to be solved. The paper used Particle Swarm Optimization (PSO) to search the best parameter X,p, then combination forecast model of PSO-GM(2,l,λ,ρ) was constructed. In order to avoid incorrect selection of inertia weight w causing the global search and local search imbalance, the paper used Decreasing Inertia Weight Particle Swarm Optimization, in which parameter w gradually decreases from 1.4 to 0.35. And agricultural machinery total power was predicted based on Zhejiang provinces statistics. Predicted results show that the combination forecast model prediction accuracy is higher than the gray GM(1,1) model and the model better fits the data. The forecast of the agricul-tural machinery total power of this combination forecast model is feasible and effective, and should be feasible in other areas of agriculture prediction.
agricultural machinery total power gray prediction particle swarm optimization background values multiple transformation
Di-yi Chen Yu-xiao Liu Xiao-yi Ma Yan Long
Electrical Engineering Department of College of Water Resources and Architectural Engineering, North Electrical Engineering Department of College of Water Resources and Architectural Engineering, North College of Mechanical and Electric Engineering, North West A&F University,712100 Yangling, Shannxi,
国际会议
南昌
英文
205-210
2010-10-22(万方平台首次上网日期,不代表论文的发表时间)