Title
Prediction Of Agricultural Machinery Total Power Based On Pso-Gm(2,1, Lambda, Rho) Model
Abstract
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 lambda to correct the background value and parameter rho for multiple transformation on the initial data, the model was expanded to GM(2,1, lambda, rho) model and prediction accuracy was improved. Because of the nonlinear traits between parameter lambda, rho and the prediction errors, they are difficult to be solved. The paper used Particle Swarm Optimization (PSO) to search the best parameter lambda, rho, then combination forecast model of PSO-GM(2,1, lambda, rho) 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 province's 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 agricultural machinery total power of this combination forecast model is feasible and effective, and should be feasible in other areas of agriculture prediction.
Year
DOI
Venue
2010
10.1007/978-3-642-18336-2_24
COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE IV, PT 2
Keywords
Field
DocType
agricultural machinery total power, gray prediction, particle swarm optimization, background values, multiple transformation
Particle swarm optimization,Applied mathematics,Nonlinear system,Agricultural machinery,Inertia,Local search (optimization),Model prediction,Mathematics
Conference
Volume
Issue
ISSN
345
PART 2
1868-4238
Citations 
PageRank 
References 
0
0.34
3
Authors
4
Name
Order
Citations
PageRank
Diyi Chen16610.01
Yu-xiao Liu200.34
Xiaoyi Ma320439.45
Long Yan420932.64