本文選題：贛南臍橙　切入點(diǎn)：BP神經(jīng)網(wǎng)絡(luò)　出處：《華中農(nóng)業(yè)大學(xué)》2017年碩士論文

【摘要】：贛南臍橙是我國(guó)重要的柑橘類水果,2015年,贛南臍橙在農(nóng)產(chǎn)品公共區(qū)域品牌價(jià)值排行榜位列第一,為提高贛南地區(qū)旅游品牌知名度,帶動(dòng)贛南蘇區(qū)人民脫貧致富,促進(jìn)柑橘產(chǎn)業(yè)轉(zhuǎn)型升級(jí)發(fā)揮了重要作用。截止到2016年,贛南臍橙種植規(guī)模逾160萬(wàn)畝,產(chǎn)業(yè)產(chǎn)值接近100億元,種植、加工、銷(xiāo)售規(guī)模逐漸增加,贛南臍橙在水果市場(chǎng)中的消費(fèi)量逐年提高,市場(chǎng)規(guī)模逐漸擴(kuò)大,然而,贛南臍橙價(jià)格波動(dòng)劇烈,短期波幅大等,易導(dǎo)致出現(xiàn)“橙貴傷民”,“橙賤傷農(nóng)”兩種極端情況。贛南臍橙價(jià)格的穩(wěn)定,關(guān)系著生產(chǎn)者、加工者、銷(xiāo)售者以及普通消費(fèi)者收益及生活的穩(wěn)定。對(duì)贛南臍橙價(jià)格波動(dòng)進(jìn)行預(yù)測(cè)研究,不僅有利于市場(chǎng)政策、銷(xiāo)售策略的制定,對(duì)于保證產(chǎn)地生產(chǎn)、農(nóng)戶收入穩(wěn)定具有十分重要的意義。本文依據(jù)價(jià)格形成理論,根據(jù)2007年-2017年贛南臍橙月度價(jià)格數(shù)據(jù),以及2016年11月-2017年2月日度價(jià)格數(shù)據(jù),分析其波動(dòng)特征及影響因素等。基于BP神經(jīng)網(wǎng)絡(luò),進(jìn)行贛南臍橙價(jià)格預(yù)測(cè)研究,并比較分析BP神經(jīng)網(wǎng)絡(luò)與ARIMA模型的預(yù)測(cè)精度,根據(jù)預(yù)測(cè)結(jié)果,分析贛南臍橙價(jià)格走勢(shì),并使用MATLAB軟件進(jìn)行實(shí)證分析。因此,本文研究?jī)?nèi)容與結(jié)論如下:(1)探究贛南臍橙產(chǎn)業(yè)發(fā)展現(xiàn)狀及價(jià)格變化;從價(jià)格波動(dòng)、產(chǎn)業(yè)結(jié)構(gòu)、市場(chǎng)推廣等方面分析了贛南臍橙產(chǎn)業(yè)面臨的問(wèn)題;從市場(chǎng)因素、自然因素、人為因素三方面探討影響贛南臍橙價(jià)格的主要因素。(2)預(yù)測(cè)贛南臍橙價(jià)格變化。根據(jù)贛南臍橙價(jià)格特征,確定適合的隱含層節(jié)點(diǎn)數(shù),搭建基于L-M改進(jìn)算法的BP神經(jīng)網(wǎng)絡(luò)價(jià)格預(yù)測(cè)模型。研究發(fā)現(xiàn),BP神經(jīng)網(wǎng)絡(luò)的預(yù)測(cè)結(jié)果誤差較低,月度價(jià)格預(yù)測(cè)相對(duì)誤差在2.5%左右。通過(guò)與ARIMA模型預(yù)測(cè)結(jié)果的對(duì)比,在日度價(jià)格預(yù)測(cè)上,BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)相對(duì)誤差為1.5%,低于ARIMA模型2%的相對(duì)誤差。(3)針對(duì)贛南臍橙價(jià)格波動(dòng)提供促進(jìn)其價(jià)格平穩(wěn)發(fā)展的對(duì)策�；趦r(jià)格預(yù)測(cè)結(jié)果及波動(dòng)原因,本文認(rèn)為贛南地區(qū)應(yīng)完善信息建設(shè),加強(qiáng)贛南臍橙價(jià)格波動(dòng)的預(yù)警研究,并提高規(guī)模化經(jīng)營(yíng)程度,降低生產(chǎn)成本,以此提高價(jià)格波動(dòng)風(fēng)險(xiǎn)的應(yīng)變能力。本文創(chuàng)新點(diǎn)主要有:(1)在研究?jī)?nèi)容上,采取最新的贛南臍橙價(jià)格數(shù)據(jù),減少了時(shí)間跨度過(guò)長(zhǎng)帶來(lái)的影響;此外,目前學(xué)術(shù)界關(guān)于水果與柑橘的價(jià)格預(yù)測(cè)研究較多,而關(guān)于贛南臍橙價(jià)格預(yù)測(cè)的研究較少。本文將近年來(lái)發(fā)展較為迅速的智能預(yù)測(cè)方法具體的應(yīng)用到贛南臍橙價(jià)格預(yù)測(cè)領(lǐng)域,不同于以往的定性分析。(2)在預(yù)測(cè)方法的選取上,比較基于L-M改進(jìn)算法的BP神經(jīng)網(wǎng)絡(luò)與標(biāo)準(zhǔn)BP網(wǎng)絡(luò)的優(yōu)度,以及BP神經(jīng)網(wǎng)絡(luò)與ARIMA預(yù)測(cè)模型的預(yù)測(cè)精度,選取合適的預(yù)測(cè)模型,避免模型單一化。
[Abstract]:Gannan navel orange is an important citrus fruit in our country. In 2015, Gannan navel orange ranked first in the list of brand value of agricultural products in public area. In order to improve the popularity of tourism brand in Gannan area and promote the people of Gannan Su District to get rid of poverty and become rich,Promoting citrus industry transformation and upgrading has played an important role.Up to 2016, the scale of navel orange planting in Gannan is over 1.6 million mu, and the industrial output value is close to 10 billion yuan. The scale of planting, processing and selling is gradually increasing. The consumption of navel orange in Gannan is increasing year by year, and the market scale is gradually expanding, however,The price fluctuation of navel orange in Gannan is violent, and the short term fluctuation is large, which may lead to two extreme conditions: "orange is expensive to injure people" and "orange is cheap to hurt agriculture".The stability of navel orange price in Gannan is related to the income and life of producers, processors, sellers and ordinary consumers.To predict the price fluctuation of navel orange in Gannan is not only beneficial to the formulation of market policy and sales strategy, but also of great significance to ensure the production of producing area and the stable income of farmers.Based on the price formation theory, the monthly price data of Gannan navel orange from 2007 to 2017 and the daily price data from November 2016 to February 2017, the fluctuation characteristics and influencing factors are analyzed.Based on BP neural network, the price prediction of navel orange in Gannan is studied, and the prediction accuracy of BP neural network and ARIMA model is compared. According to the forecast results, the price trend of navel orange in south Jiangxi is analyzed, and the empirical analysis is carried out with MATLAB software.Therefore, the contents and conclusions of this paper are as follows: (1) to explore the present situation and price change of the navel orange industry in Gannan; to analyze the problems faced by the navel orange industry in Gannan from the aspects of price fluctuation, industrial structure and market promotion; to analyze the market factors, natural factors, and so on;The main factors influencing the price of Gannan navel orange were discussed from three aspects of human factors. (2) to predict the price change of navel orange in south Jiangxi.According to the price characteristics of navel orange in Gannan, the suitable number of hidden layer nodes is determined, and the BP neural network price prediction model based on L-M improved algorithm is built.It is found that the prediction error of BP neural network is low and the relative error of monthly price prediction is about 2.5%.By comparing with the prediction results of ARIMA model, the relative error of BP neural network in daily price prediction is 1.5, which is less than 2% of ARIMA model.Based on the result of price forecast and the reason of fluctuation, this paper thinks that the information construction should be perfected, the early warning study of price fluctuation of navel orange should be strengthened, the degree of large-scale operation should be improved, and the production cost should be reduced.In order to improve the risk of price fluctuations in response to the ability.The main innovation of this paper is: (1) in the research content, we adopt the latest price data of navel orange in Gannan to reduce the influence of long time span. In addition, there are more researches on the price prediction of fruits and oranges in academic circles at present.However, there is little research on the price prediction of navel orange in Gannan.In this paper, the intelligent forecasting method, which has been developed rapidly in recent years, has been applied to the price prediction of navel orange in south Jiangxi, which is different from the qualitative analysis in the past.The advantages of BP neural network based on L-M improved algorithm and standard BP neural network, as well as the prediction accuracy of BP neural network and ARIMA prediction model are compared. The suitable prediction model is selected to avoid the simplification of the model.
【學(xué)位授予單位】：華中農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP183;F323.7

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