基于RBF神经网络的股票市场预测

基于RBF神经网络的股票市场预测
基于RBF神经网络的股票市场预测

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万方数据

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基于Bp神经网络的股票预测

基于B p神经网络的股 票预测 IMB standardization office【IMB 5AB- IMBK 08- IMB 2C】

基于神经网络的股票预测 【摘要】: 股票分析和预测是一个复杂的研究领域,本论文将股票技术分析理论与人工神经网络相结合,针对股票市场这一非线性系统,运用BP神经网络,研究基于历史数据分析的股票预测模型,同时,对单只股票短期收盘价格的预测进行深入的理论分析和实证研究。本文探讨了BP神经网络的模型与结构、BP算法的学习规则、权值和阈值等,构建了基于BP神经网络的股票短期预测模型,研究了神经网络的模式、泛化能力等问题。并且,利用搭建起的BP神经网络预测模型,采用多输入单输出、单隐含层的系统,用前五天的价格来预测第六天的价格。对于网络的训练,选用学习率可变的动量BP算法,同时,对网络结构进行了隐含层节点的优化,多次尝试,确定最为合理、可行的隐含层节点数,从而有效地解决了神经网络隐含层节点的选取问题。 【abstract] ,,makingin-depththeoreticalanalysisandempiricalstudiesontheshort-termclosingpriceforecastsofsinglestock. Secondly,makingresearchonthemodelandstructureofBPneuralnetwork, learningrules,weightsofBPalgorithmandsoon,buildingastockshort-termforecastingmodelbasedontheBPneuralnetwork,,usingsystemofmultiple-inputsingle-outputandsinglehiddenlayer,,. 【关键词】BP神经网络股票预测分析 1.引言 股票市场是一个不稳定的非线性动态变化的复杂系统,股价的变动受众多因素的影响。影响股价的因素可简单地分为两类,一类是公司基本面的因素,另一类是股票技术面的因

基于神经网络的股票价格走势预测及其MATLAB实现——论文

基于神经网络的股票价格走势预测及其MATLAB实现 摘要 伴随着我国经济的高速发展和广大投资者日益旺盛的需求,股票投资已经成为一种常见的投资手段,而股票价格预测也逐渐成为广大投资者关心和研究的重点问题。股票价格的波动是一个高度复杂化的非线性动态系统,其本身具有诸如大规模数据、噪声、模糊非线性等特点。针对这些特点本文在深入分析股票市场实际预测中所面临的关键问题和比较各种已有的股票预测方法的基础上,探讨运用神经网络这一人工智能工具,研究基于历史数据分析的股票预测模型。 神经网络是建立在对大规模的股票历史数据的学习仿真的基础上,运用黑盒预测方式找出股市波动的内在规律,并通过将其存储在网络的权值、阈值中,以此来预测未来短期或是中长期的价格走势。 关键字:神经网络,股票,预测,MATLAB工具箱 ABSTRACT Along with the economy growth and increasingly strong demand of many investors in our country, stock has become a common means of investment, and stock price forecast has greatly been one of the focuses of study topic. The change of stock price is a highly complicated nonlinear dynamic system, itself has many characteristics such as massive data, noise, fuzzy and nonlinear. This article analyses the key issues being existent in the real stock market prediction and compares various existing stock forecasting methods. We will try to research on stock price prediction model based on a neural network with huge historical data. Neural network is based on studying massive historical data, uses the black box of forecasting ways to find the internal disciplinarian of stock market, and stores them in the weights and valves values of the neural network for predicting the short-term or long-term trend in the future. KEYWORD:Neural networks, Stock, prediction, MATLAB toolbox

基于Bp神经网络的股票预测

基于神经网络的股票预测 【摘要】: 股票分析和预测是一个复杂的研究领域,本论文将股票技术分析理论与人工神经网络相结合,针对股票市场这一非线性系统,运用BP神经网络,研究基于历史数据分析的股票预测模型,同时,对单只股票短期收盘价格的预测进行深入的理论分析和实证研究。本文探讨了BP神经网络的模型与结构、BP算法的学习规则、权值和阈值等,构建了基于BP神经网络的股票短期预测模型,研究了神经网络的模式、泛化能力等问题。并且,利用搭建起的BP神经网络预测模型,采用多输入单输出、单隐含层的系统,用前五天的价格来预测第六天的价格。对于网络的训练,选用学习率可变的动量BP算法,同时,对网络结构进行了隐含层节点的优化,多次尝试,确定最为合理、可行的隐含层节点数,从而有效地解决了神经网络隐含层节点的选取问题。 【abstract] Stock analysis and forecasting is a complex field of study. The paper will make research on stock prediction model based on the analysis of historical data, using BP neural network and technical analysis theory. At the same time, making in-depth theoretical analysis and empirical studies on the short-term closing price forecasts of single stock. Secondly, making research on the model and structure of BP neural network, learning rules, weights of BP algorithm and so on, building a stock short-term forecasting model based on the BP neural network, related with the model of neural network and the ability of generalization. Moreover, using system of multiple-input single-output and single hidden layer, to forecast the sixth day price by BP neural network forecasting model structured. The network of training is chosen BP algorithm of traingdx, while making optimization on the node numbers of the hidden layer by several attempts. Thereby resolve effectively the problem of it. 【关键词】BP神经网络股票预测分析 1.引言 股票市场是一个不稳定的非线性动态变化的复杂系统,股价的变动受众多因素的影响。影响股价的因素可简单地分为两类,一类是公司基本面的因素,另一类是股票技术面的因素,虽然股票的价值是公司未来现金流的折现,由公司的基本面所决定,但是由于公司基本面的数据更新时间慢,且很多时候并不能客观反映公司的实际状况,采用适当数学模型就能在一定

基于Bp神经网络的股票预测

深圳大学 神经网络原理课程实验 题目:基于BP神经网络的股票预测姓名: 专业: 学院: 信息工程学院 指导教师: 职称: 2014年5月17日

基于神经网络的股票预测 【摘要】: 股票分析和预测是一个复杂的研究领域,本论文将股票技术分析理论与人工神经网络相结合,针对股票市场这一非线性系统,运用BP神经网络,研究基于历史数据分析的股票预测模型,同时,对单只股票短期收盘价格的预测进行深入的理论分析和实证研究。本文探讨了BP神经网络的模型与结构、BP算法的学习规则、权值和阈值等,构建了基于BP神经网络的股票短期预测模型,研究了神经网络的模式、泛化能力等问题。并且,利用搭建起的BP神经网络预测模型,采用多输入单输出、单隐含层的系统,用前五天的价格来预测第六天的价格。对于网络的训练,选用学习率可变的动量BP算法,同时,对网络结构进行了隐含层节点的优化,多次尝试,确定最为合理、可行的隐含层节点数,从而有效地解决了神经网络隐含层节点的选取问题。 【abstract] Stock analysis and forecasting is a complex field of study. The paper will make research on stock prediction model based on the analysis of historical data, using BP neural network and technical analysis theory. At the same time, making in-depth theoretical analysis and empirical studies on the short-term closing price forecasts of single stock. Secondly, making research on the model and structure of BP neural network, learning rules, weights of BP algorithm and so on, building a stock short-term forecasting model based on the BP neural network, related with the model of neural network and the ability of generalization. Moreover, using system of multiple-input single-output and single hidden layer, to forecast the sixth day price by BP neural network forecasting model structured. The network of training is chosen BP algorithm of traingdx, while making optimization on the node numbers of the hidden layer by several attempts. Thereby resolve effectively the problem of it. 【关键词】BP神经网络股票预测分析 1.引言 股票市场是一个不稳定的非线性动态变化的复杂系统,股价的变动受众多因素的影响。影响股价的因素可简单地分为两类,一类是公司基本面的因素,另一类是股票技术面的因素,虽然股票的价值是公司未来现金流的折现,由公司的基本面所决定,但是由于公司基本面的数据更新时间慢,且很多时候并不能客观反映公司的实际状况,采用适当数学模型就能在一定

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