Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

Yan Xie; Mu Li; Jin Zhou; Chang-zheng Zheng

doi:10.1117/12.836636

10 July 2009 Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

Yan Xie, Mu Li, Jin Zhou, Chang-zheng Zheng

Author Affiliations +

Proceedings Volume 7490, PIAGENG 2009: Intelligent Information, Control, and Communication Technology for Agricultural Engineering; 74901M (2009) https://doi.org/10.1117/12.836636
Event: International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2009), 2009, Zhangjiajie, China

Abstract

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

Citation Download Citation

Yan Xie, Mu Li, Jin Zhou, and Chang-zheng Zheng "Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm", Proc. SPIE 7490, PIAGENG 2009: Intelligent Information, Control, and Communication Technology for Agricultural Engineering, 74901M (10 July 2009); https://doi.org/10.1117/12.836636

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