摘要
对灾害年份粮食产量进行准确预测,对于保障粮食安全具有重要的意义。由于粮情具有随机性,传统算法过于依赖人工经验,不能克服灾害年份粮食产量的无序性变化,从而降低了预测结果的可信度。提出一种采用马尔科夫灰度模型的灾害年份粮食产量预测模型。将灰度模型和马尔科夫模型相结合,建立灾害年份粮食产量的预测模型,对灾害年份影响因素状态变化的趋势进行准确估计,并消除灰度模型波动性较大年份的粮食预测情况,获得准确的预测结果。仿真结果表明,利用改进算法进行灾害年份粮食产量预测,可为提高预测结果准确度提供科学依据。
To forecast the disaster year food production is of great importance to ensure food safety. Traditional algorithms rely too much on human experience, cannot overcome the disorder of disaster year food production changes, which reduces the credibility of results. For this, put forward a model based on markov gray disaster year grain output prediction model. Combining gray model and markov model, to establish the forecast model of disaster year food pro- duction, the year of disaster factors influencing the trend of the change of state accurately estimates that year of vola- tile food and eliminate gray model, obtain accurate prediction results. Simulation experiment results show that the im- proved algorithm disaster year grain output prediction, can improve the prediction results, the effect is satisfactory.
出处
《计算机仿真》
CSCD
北大核心
2016年第1期446-449,共4页
Computer Simulation
关键词
大数据
灾害年份
粮食预测
Big data
Year of disaster
Prediction of grain
