發(fā)布時間：2019-05-10 22:53

【摘要】：民以食為天,糧食問題自古以來就是關(guān)系人類社會發(fā)展的重要問題。我國又是人多地少的發(fā)展中國家,經(jīng)濟(jì)發(fā)展給人民生活帶來富裕的同時,也給我國的糧食供應(yīng)造成壓力。糧食供需與糧食安全問題緊密相連,糧食安全問題不僅僅是經(jīng)濟(jì)問題,更關(guān)系到國家的長久發(fā)展,因此需要對我國糧食產(chǎn)量進(jìn)行有效預(yù)測,從而合理解決糧食供需平衡問題,確保國家糧食安全。本文在深入了解我國糧食現(xiàn)狀后,著重對糧食產(chǎn)量的預(yù)測開展工作。首先,對時間序列分析中常用的ARIMA模型進(jìn)行改進(jìn),同時分別采用改進(jìn)模型和傳統(tǒng)模型對不同時間區(qū)間的產(chǎn)量數(shù)據(jù)進(jìn)行短期預(yù)測,結(jié)果表明,當(dāng)選取較長時間區(qū)間,并且采用改進(jìn)模型進(jìn)行預(yù)測時,預(yù)測結(jié)果越準(zhǔn)確。其次,提出了聯(lián)合動態(tài)預(yù)測模型。對糧食產(chǎn)量的主要影響因子進(jìn)行分析,然后根據(jù)關(guān)聯(lián)度選擇主要因子與產(chǎn)量數(shù)據(jù)共同構(gòu)建模型,對選中的影響因子采用改進(jìn)的ARIMA模型進(jìn)行動態(tài)預(yù)測,再結(jié)合多元回歸實(shí)現(xiàn)糧食產(chǎn)量的中長期動態(tài)預(yù)測。最后,考慮到產(chǎn)量數(shù)據(jù)的非線性特征,以及模型參數(shù)優(yōu)化等問題,本文結(jié)合統(tǒng)計學(xué)習(xí)理論的相關(guān)內(nèi)容,分析并學(xué)習(xí)了最小二乘支持向量機(jī)的應(yīng)用原理和粒子群算法的全局尋優(yōu)特性,提出了基于粒子群優(yōu)化的最小二乘支持向量機(jī)模型,二者的有效結(jié)合使得預(yù)測模型既有更快的求解速度,又能保證參數(shù)選擇全局最優(yōu),并且在數(shù)據(jù)預(yù)處理階段加入了平滑處理,相較于未平滑處理具有更高的預(yù)測精度。實(shí)驗(yàn)結(jié)果表明:改進(jìn)的ARIMA模型和聯(lián)合動態(tài)預(yù)測模型能夠有效的實(shí)現(xiàn)糧食產(chǎn)量的短期和中長期預(yù)測,并且后者的預(yù)測精度相較于前者以及傳統(tǒng)灰色模型都有所提升�；诹Ｗ尤簝�(yōu)化的最小二乘支持向量機(jī)模型較好的解決了非線性問題,在對糧食產(chǎn)量進(jìn)行短期預(yù)測時預(yù)測精度更好。
[Abstract]:Since ancient times, the food problem has been an important issue related to the development of human society. China is a developing country with a large population and a small land. economic development not only brings wealth to the people's life, but also puts pressure on the food supply of our country. The problem of grain supply and demand is closely related to the problem of food security. The problem of food security is not only an economic problem, but also related to the long-term development of the country. Therefore, it is necessary to effectively predict the grain output of our country, so as to reasonably solve the problem of the balance of grain supply and demand. Ensure national food security. After deeply understanding the present situation of grain in China, this paper focuses on the prediction of grain output. First of all, the ARIMA model, which is commonly used in time series analysis, is improved, and the improved model and the traditional model are used to predict the yield data in different time intervals respectively. the results show that when the longer time interval is selected, When the improved model is used for prediction, the more accurate the prediction results are. Secondly, a joint dynamic prediction model is proposed. The main influencing factors of grain yield are analyzed, and then the model is constructed according to the correlation degree selection of the main factors and the yield data, and the selected influencing factors are dynamically predicted by the improved ARIMA model. Combined with multiple regression, the medium and long term dynamic prediction of grain yield is realized. Finally, considering the nonlinear characteristics of yield data and the optimization of model parameters, this paper combines the relevant contents of statistical learning theory. The application principle of least squares support vector machine and the global optimization characteristics of particle swarm optimization algorithm are analyzed and learned, and the least square support vector machine model based on particle swarm optimization is proposed. The effective combination of the two makes the prediction model not only solve faster, but also ensure the global optimization of parameter selection, and the smoothing processing is added in the data preprocessing stage, which has higher prediction accuracy than the unsmoothed processing. The experimental results show that the improved ARIMA model and the joint dynamic prediction model can effectively realize the short-term and medium-and long-term prediction of grain yield, and the prediction accuracy of the latter is higher than that of the former and the traditional grey model. The least square support vector machine model based on particle swarm optimization solves the nonlinear problem well, and the prediction accuracy is better when predicting grain yield in the short term.
【學(xué)位授予單位】：河南工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F326.11;O211.61

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