發(fā)布時(shí)間：2018-03-12 15:10

  本文選題：模糊邏輯　切入點(diǎn)：神經(jīng)網(wǎng)絡(luò)　出處：《安徽財(cái)經(jīng)大學(xué)》2012年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著我國(guó)經(jīng)濟(jì)體制改革和金融體制改革的深入,股票投資已經(jīng)成為了社會(huì)生活的一個(gè)重要部分。股票價(jià)格的預(yù)測(cè)成為投資者關(guān)心和研究的重點(diǎn)。但股票市場(chǎng)是一個(gè)極其復(fù)雜的非線(xiàn)性動(dòng)力學(xué)系統(tǒng),具有高噪聲、嚴(yán)重非線(xiàn)性和投資者的盲目任意性等因素以及各因素間的相關(guān)性錯(cuò)綜復(fù)雜,造成其價(jià)格的波動(dòng)往往表現(xiàn)出較強(qiáng)的非線(xiàn)性特征。另外,股市的建模和預(yù)測(cè)所處理的信息量往往是十分龐大的,對(duì)算法的要求很高,正是由于其復(fù)雜的非線(xiàn)性特征,使得關(guān)于股市預(yù)測(cè)的結(jié)果往往難如人意。如何建立一個(gè)運(yùn)算速度和精確度都比較高的股市預(yù)測(cè)模型,對(duì)于金融投資者具有理論意義和實(shí)際應(yīng)用價(jià)值。 本文針對(duì)在股票價(jià)格預(yù)測(cè)中存在的困難引入模糊邏輯和神經(jīng)網(wǎng)絡(luò)的概念,利用模糊邏輯中可以用模糊性的自然語(yǔ)言表現(xiàn)知識(shí)和可以用Max, Min這類(lèi)簡(jiǎn)單運(yùn)算實(shí)現(xiàn)知識(shí)的模糊推理的特點(diǎn),以及利用神經(jīng)網(wǎng)絡(luò)中能夠生成不需要明確表現(xiàn)知識(shí)的規(guī)則和其強(qiáng)大的自學(xué)能力的特點(diǎn),把二者結(jié)合起來(lái)構(gòu)成模糊神經(jīng)網(wǎng)絡(luò)。利用TS模糊規(guī)則和前饋神經(jīng)網(wǎng)絡(luò)的方法進(jìn)行建模,并且探討了網(wǎng)絡(luò)的結(jié)構(gòu)、隱節(jié)點(diǎn)個(gè)數(shù)確定的原則、樣本數(shù)據(jù)的選取和處理、初始參數(shù)的確定等問(wèn)題。 根據(jù)模糊神經(jīng)網(wǎng)絡(luò)對(duì)股票預(yù)測(cè)的原理,建立基于模糊神經(jīng)網(wǎng)絡(luò)的股市預(yù)測(cè)模型,并利用相關(guān)性分析對(duì)股票預(yù)測(cè)時(shí)的輸入項(xiàng)進(jìn)行了篩選。通過(guò)MATLAB7.0軟件,對(duì)選取的綠景地產(chǎn)、濰柴動(dòng)力、招商證券、寶鋼股份和上證指數(shù)進(jìn)行實(shí)證分析,根據(jù)神經(jīng)網(wǎng)絡(luò)常用的預(yù)測(cè)性能的評(píng)價(jià)指標(biāo)對(duì)預(yù)測(cè)結(jié)果進(jìn)行了評(píng)價(jià),證實(shí)了該模糊神經(jīng)網(wǎng)絡(luò)進(jìn)行預(yù)測(cè)是有效的,預(yù)測(cè)系統(tǒng)是成功的。
[Abstract]:With the deepening of China's economic and financial system reform, Stock investment has become an important part of social life. The prediction of stock price has become the focus of investors' attention and research. But the stock market is an extremely complex nonlinear dynamic system with high noise. Factors such as severe nonlinearity, blind arbitrariness of investors, and the correlation among various factors are complicated, resulting in their price fluctuations often showing strong nonlinear characteristics. In addition, Stock market modeling and forecasting often deal with a huge amount of information, and the algorithm is very demanding, precisely because of its complex nonlinear characteristics. How to establish a stock market forecasting model with high calculation speed and accuracy is of theoretical significance and practical application value for financial investors. In this paper, the concepts of fuzzy logic and neural network are introduced to solve the difficulties in stock price prediction. The characteristics of fuzzy logic can be used to express knowledge in natural language and simple operations such as Maxand Min can be used to realize fuzzy reasoning of knowledge. Using the characteristics of the neural network which can generate the rules which do not need to express the knowledge clearly and its powerful self-study ability, the fuzzy neural network is constructed by combining the two features. The model is modeled by using TS fuzzy rules and feedforward neural networks. The structure of the network, the principle of determining the number of hidden nodes, the selection and processing of sample data, and the determination of initial parameters are discussed. According to the principle of stock forecasting based on fuzzy neural network, the stock market forecasting model based on fuzzy neural network is established, and the input items in stock forecasting are screened by correlation analysis. The selected green scene real estate is selected by MATLAB7.0 software. Weichai Power, China Merchants Securities, Baosteel shares and Shanghai Stock Exchange Index are empirically analyzed, and the prediction results are evaluated according to the commonly used performance evaluation indexes of neural networks. It is proved that the fuzzy neural network is effective in forecasting. The prediction system is successful.
【學(xué)位授予單位】：安徽財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：F832.51;TP18

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本文編號(hào)：1602102