Prediction of Agricultural Machinery Total Power Based on PSO-GM(2,1,λ, ρ) 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 λ to correct the background value and parameter ρ 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 λ,ρ and the prediction errors, they are difficult to be solved. The paper used Particle Swarm Optimization (PSO) to search the best parameter λ,ρ, then combination forecast model of PSO-GM(2,1, 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 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.
Type de document :
Communication dans un congrès
Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-345 (Part II), pp.205-210, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18336-2_24〉
Liste complète des métadonnées

Littérature citée [10 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01562808
Contributeur : Hal Ifip <>
Soumis le : lundi 17 juillet 2017 - 09:50:58
Dernière modification le : mardi 18 juillet 2017 - 15:38:26
Document(s) archivé(s) le : vendredi 26 janvier 2018 - 23:22:57

Fichier

978-3-642-18336-2_24_Chapter.p...
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Di-Yi Chen, Yu-Xiao Liu, Xiao-Yi Ma, Yan Long. Prediction of Agricultural Machinery Total Power Based on PSO-GM(2,1,λ, ρ) Model. Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-345 (Part II), pp.205-210, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18336-2_24〉. 〈hal-01562808〉

Partager

Métriques

Consultations de la notice

80

Téléchargements de fichiers

85