Analysis of Grain Condition in Improved Granary Based on Grey Prediction Algorithm

Huichao Zhang, Guangyuan Zhao, X. Qin
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Abstract

Scientific grain storage plays an important role in ensuring food security and promoting high-efficiency energy-saving operations. The paper provides more accurate reference datas for grain storage work. It can easily monitor the grain situation during the reserve period, and can scientifically predict the future grain development trend more accurately. It takes countermeasure in advance to prevent food disaster and further reduce. The workload of the warehouse clerk and related staff, while ensuring the safe and stable operation of the grain storage. Compared with the traditional Gery Model, the residual correction method is proposed to improve the data prediction accuracy. Combined with the Grey Verhulst model, a new residual-corrected Verhulst model is proposed. The simulation prove that the improved model is more traditional than the traditional one. The model is more conducive to the prediction of volatility data and the prediction accuracy is greatly improved.
基于灰色预测算法的改进型粮仓粮食状况分析
科学储粮对保障粮食安全、促进高效节能运行具有重要作用。为粮食仓储工作提供了更为准确的参考数据。可以方便地监测储备期间的粮食形势,更准确地科学预测未来粮食发展趋势。提前采取对策,预防粮食灾害,进一步减少粮食灾害。仓库文员及相关人员的工作量,同时保证粮库的安全稳定运行。与传统的格里模型相比,提出了残差校正方法,提高了数据的预测精度。结合灰色Verhulst模型,提出了一种新的残差校正Verhulst模型。仿真结果表明,改进后的模型比传统模型更具传统性。该模型更有利于波动性数据的预测,预测精度大大提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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