本文選題：支持向量機(jī)　切入點(diǎn)：等級(jí)分類　出處：《南京農(nóng)業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：耕地土壤質(zhì)量與肥力受到越來越多的關(guān)注,土壤有機(jī)質(zhì)(Soil Organic Matter,SOM)作為土壤重要的養(yǎng)分來源之一,也成為研究的熱點(diǎn)。土壤有機(jī)質(zhì)含量預(yù)測是根據(jù)長期定位實(shí)驗(yàn)點(diǎn)的土壤有機(jī)質(zhì)的含量變化進(jìn)行預(yù)測,為研究土壤有機(jī)質(zhì)提供科學(xué)理論依據(jù)。土壤養(yǎng)分評(píng)價(jià)是耕地土壤質(zhì)量與土壤肥力評(píng)價(jià)的重要組成部分,常用的土壤養(yǎng)分指標(biāo)為:土壤有機(jī)質(zhì),土壤全氮(Soil TotalNitrogen,TN),土壤全磷(Soil Total Phosphorus,TP),土壤全鉀(Soil Total Potassium,TK),土壤速效氮(Soil Available Nitrogen,AN),土壤速效磷(Soil Available Phosphorus,AP),土壤速效鉀(Soil Available Potassium,AK)的含量。在各種養(yǎng)分含量的基礎(chǔ)上,分析耕地土壤質(zhì)量與肥力的等級(jí),可以為研究者提供科學(xué)合理的開發(fā)和管理土地資源的根據(jù)。目前數(shù)學(xué)建模在非數(shù)學(xué)領(lǐng)域也應(yīng)用廣泛,其中模型評(píng)價(jià)是數(shù)學(xué)建模的應(yīng)用之一,例如綜合指數(shù)法、人工神經(jīng)網(wǎng)絡(luò)法、模糊數(shù)學(xué)法、不同距離聚類法都是被大家廣泛應(yīng)用的方法。然而,這些模型不適合復(fù)雜非線性關(guān)系的因素的評(píng)價(jià)和土壤肥力水平的表現(xiàn),需要在評(píng)估的過程中調(diào)整權(quán)重,影響了評(píng)價(jià)模型的覆蓋度和結(jié)果的可靠性。近年來發(fā)展起來的支持向量機(jī)(Support Vector Machine,SVM)技術(shù)是在數(shù)學(xué)統(tǒng)計(jì)學(xué)習(xí)理論基礎(chǔ)上發(fā)展起來的一種新型的機(jī)器學(xué)習(xí)技術(shù),為實(shí)現(xiàn)上述目標(biāo)提供了有效方法。該技術(shù)從VC維理論和結(jié)構(gòu)風(fēng)險(xiǎn)最小化準(zhǔn)則(SRM)的角度出發(fā),保證模型能達(dá)到全局最優(yōu),具有最大泛化能力和強(qiáng)大的推廣能力,能應(yīng)用和解決許多預(yù)測問題,已成為機(jī)器學(xué)習(xí)領(lǐng)域頗有影響的成果之一。該研究在熟知支持向量機(jī)理論的前提下,與土壤生態(tài)相結(jié)合,做到理論與實(shí)際相結(jié)合:1、應(yīng)用支持向量機(jī)分類理論評(píng)價(jià)湖南祁陽不施肥下紅壤基礎(chǔ)肥力等級(jí)。分析不同處理的核函數(shù)類型的支持向量機(jī)下土壤化學(xué)性質(zhì)的實(shí)驗(yàn)數(shù)據(jù)。分析結(jié)果表明,支持向量機(jī)理論用于土壤基礎(chǔ)肥力等級(jí)評(píng)價(jià)是可行的,并且還表明核函數(shù)類型對土壤基礎(chǔ)肥力的類別不起決定作用。比較支持向量機(jī)模型分類結(jié)果與其另外三種評(píng)價(jià)方法(BP神經(jīng)網(wǎng)絡(luò)模型,判別法,聚類分析法)的分類結(jié)果,表明用支持向量機(jī)分類模型進(jìn)行土壤基礎(chǔ)肥力評(píng)價(jià)的結(jié)果與實(shí)測分類結(jié)果更可靠。2、應(yīng)用支持向量機(jī)回歸理論預(yù)測安徽阜陽土壤有機(jī)質(zhì)的含量變化。實(shí)驗(yàn)數(shù)據(jù)通過支持向量機(jī)回歸理論的方法與反向傳播(BP)神經(jīng)網(wǎng)絡(luò)和徑向基函數(shù)(RBF)神經(jīng)網(wǎng)絡(luò)相對照分析得出支持向量機(jī)模型結(jié)果更加精確。對土壤有機(jī)質(zhì)含量和產(chǎn)量進(jìn)行回歸模擬,結(jié)果表明土壤有機(jī)質(zhì)和作物產(chǎn)量呈正相關(guān)關(guān)系。3、為提高實(shí)驗(yàn)設(shè)計(jì)的完善性和數(shù)據(jù)的可靠性,本文提出了一種新型支持向量機(jī)預(yù)測和分類的土壤肥力分級(jí)模型一多重混合支持向量機(jī)模型。本研究將新型的機(jī)器學(xué)習(xí)方法-支持向量機(jī)方法應(yīng)用于土壤生態(tài)領(lǐng)域,進(jìn)行土壤基礎(chǔ)肥力評(píng)價(jià)和土壤有機(jī)質(zhì)含量預(yù)測,突出顯示支持向量機(jī)方法的可行性和優(yōu)越性。
[Abstract]:Soil quality and soil fertility of cultivated land has attracted more and more attention, the soil organic matter (Soil Organic, Matter, SOM) as one of the important sources of soil nutrients, has become a research hotspot. The prediction of soil organic matter content is predicted according to the change of soil organic matter content of the long-term experiment points, to provide scientific theoretical basis for the study of soil organic quality. Soil nutrient evaluation is an important part of soil quality and soil fertility evaluation of cultivated land, soil nutrient indexes commonly used for soil organic matter, soil total nitrogen (Soil, TotalNitrogen, TN), soil total phosphorus (Soil Total, Phosphorus, TP), soil total potassium (Soil Total Potassium, TK), soil available nitrogen (Soil Available Nitrogen, AN), soil available phosphorus (Soil Available, Phosphorus, AP), soil available potassium (Soil Available Potassium, AK) content. Based on the nutrient content of the soil, analysis of cultivated land Soil quality and soil fertility level, can provide scientific and reasonable development and management of land resources for researchers at present. According to the mathematical modeling in non mathematics fields are also widely used. The evaluation model is one of the mathematical modeling of the application, such as comprehensive index method, artificial neural network method, fuzzy mathematics method, clustering method is different from we are the widely used methods. However, these factors evaluation model is not suitable for complex nonlinear relationship and the soil fertility level, need to adjust the weights in the assessment process, affecting the reliability of the evaluation model and coverage results. Support vector machine developed in recent years (Support Vector Machine, SVM) technology is the development of learning theory in mathematical statistics. It is a new machine learning technique, provides an effective method to achieve the above goals. The technology from the VC dimension theory and knot Structural risk minimization (SRM) point of view, the model can achieve global optimal, with maximum generalization ability and strong generalization ability, and can be used to solve many problems of prediction, has become one of the most influential achievements in the field of machine learning. The research premise to vector machine theory as well as support, combined with soil ecology, achieve the combination of theory and practice: 1, evaluation of red soil fertility grade based Hunan Qiyang fertilizer under the application of support vector machine classification theory. The analysis of the experimental data of soil chemical properties of kernel function of support vector machine for different types of treatment. The analysis results show that the support vector machine theory for soil fertility evaluation is feasible., and also show that the category type of kernel function to soil fertility does not play a decisive role. The model of support vector machine classification results and other three kinds of evaluation methods (BP The neural network model, discriminant analysis, cluster analysis) classification results, the evaluation result showed that the soil fertility by using SVM model results and the measured results are more reliable classification.2, using support vector machine to predict the change of soil organic matter content in Anhui Fuyang regression theory. The experimental data with the back propagation through the method of support vector machine regression theory (BP) neural network and radial basis function (RBF) neural network according to the analysis of support vector machine model results more accurate. The soil organic matter content and yield regression simulation, the results show that the soil organic matter and crop yield was positively related to.3, in order to improve the reliability of experimental design and data integrity in this paper, proposed a soil fertility grading model of a novel support vector machine prediction and classification of heavy hybrid support vector machine model. This research will be the new type of machine Support vector machine (SVM) is applied in the field of soil ecology to evaluate soil fertility and predict soil organic matter content, highlighting the feasibility and superiority of support vector machine.

【學(xué)位授予單位】：南京農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：S158;S153.621;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 李波;;支持向量機(jī)在高校教學(xué)質(zhì)量評(píng)價(jià)中的應(yīng)用研究[J];計(jì)算機(jī)仿真;2011年10期

2 楊柳;;支持向量機(jī)方法在地震短期預(yù)測中的應(yīng)用[J];華北地震科學(xué);2010年03期

3 劉偉;孫林;;基于支持向量機(jī)的課堂教學(xué)質(zhì)量評(píng)價(jià)[J];合肥工業(yè)大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年07期

4 胡蓉;;多輸出支持向量回歸及其在股指預(yù)測中的應(yīng)用[J];計(jì)算機(jī)技術(shù)與發(fā)展;2007年10期

5 張義豐,王又豐,劉錄祥,侯向陽;中國北方旱地農(nóng)業(yè)研究進(jìn)展與思考[J];地理研究;2002年03期

，

本文編號(hào)：1591734