灰色马尔可夫数学模型在陕西省粮食产量预测中的应用  

Application of Grey Markov Mathematical Model in Grain Yield Prediction in Shaanxi Province

作　　者：郭敏 尚朋辉 GUO Min;SHANG Penghui(Xi'an Siyuan University,Xi'an 710038,China;Xi'an Vocational and Technical College,Xi'an 710016,China)

机构地区：[1]西安思源学院基础部,陕西西安710038 [2]西安职业技术学院,陕西西安710016

出　　处：《现代食品》2025年第4期1-3,共3页Modern Food

摘　　要：粮食产量系统具有随机性、非线性与动态性,传统单一预测方法难以精准进行中长期预测,且精度较低。因此,本研究结合灰色GM(1,1)与马尔可夫模型预测陕西粮食产量。依据陕西粮食产量历史数据构建GM(1,1)模型,捕捉潜在趋势规律;借助马尔可夫模型特性,考虑系统状态随机转移修正预测值。研究发现,相较于单一的灰色预测模型,灰色马尔可夫模型既能精准捕捉时间序列长期潜在趋势,又能凭状态转移概率刻画波动数据,其在波动大、规律复杂的中长期预测时,展现出精度较高的优势。The grain yield system has randomness,nonlinearity,and dynamism,and traditional single prediction methods are difficult to accurately predict in the medium and long term,with low accuracy.Therefore,this study combines grey GM(1,1)and Markov model to predict grain yield in Shaanxi.Based on historical data of grain production in Shaanxi,a GM(1,1)model is constructed to capture potential trend patterns;By utilizing the characteristics of Markov models,the system state random transition is considered to correct the predicted values.Research has found that compared to a single grey prediction model,the grey Markov model can accurately capture long-term potential trends in time series and characterize fluctuating data based on state transition probabilities.It demonstrates the advantage of high accuracy in medium-and long-term predictions with large fluctuations and complex patterns.

关 键 词：粮食产量 灰色预测 马尔可夫链 

分 类 号：S117[农业科学—农业基础科学]