本文選題：模糊邏輯　切入點：神經網絡　出處：《安徽財經大學》2012年碩士論文　論文類型：學位論文

【摘要】：隨著我國經濟體制改革和金融體制改革的深入,股票投資已經成為了社會生活的一個重要部分。股票價格的預測成為投資者關心和研究的重點。但股票市場是一個極其復雜的非線性動力學系統(tǒng),具有高噪聲、嚴重非線性和投資者的盲目任意性等因素以及各因素間的相關性錯綜復雜,造成其價格的波動往往表現出較強的非線性特征。另外,股市的建模和預測所處理的信息量往往是十分龐大的,對算法的要求很高,正是由于其復雜的非線性特征,使得關于股市預測的結果往往難如人意。如何建立一個運算速度和精確度都比較高的股市預測模型,對于金融投資者具有理論意義和實際應用價值。 本文針對在股票價格預測中存在的困難引入模糊邏輯和神經網絡的概念,利用模糊邏輯中可以用模糊性的自然語言表現知識和可以用Max, Min這類簡單運算實現知識的模糊推理的特點,以及利用神經網絡中能夠生成不需要明確表現知識的規(guī)則和其強大的自學能力的特點,把二者結合起來構成模糊神經網絡。利用TS模糊規(guī)則和前饋神經網絡的方法進行建模,并且探討了網絡的結構、隱節(jié)點個數確定的原則、樣本數據的選取和處理、初始參數的確定等問題。 根據模糊神經網絡對股票預測的原理,建立基于模糊神經網絡的股市預測模型,并利用相關性分析對股票預測時的輸入項進行了篩選。通過MATLAB7.0軟件,對選取的綠景地產、濰柴動力、招商證券、寶鋼股份和上證指數進行實證分析,根據神經網絡常用的預測性能的評價指標對預測結果進行了評價,證實了該模糊神經網絡進行預測是有效的,預測系統(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.
【學位授予單位】：安徽財經大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F832.51;TP18

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本文編號：1602102