【作者】 南敏；

【导师】 叶德谦；

【作者基本信息】 青岛理工大学 ， 计算机应用， 2010， 硕士

【摘要】 人们致力于寻找各种有效方法来规避股票市场风险和获得高收益,为此提出了许多分析股票价格运行趋势的技术。由于股票市场极为复杂,影响因素很多,导致每种技术在实际应用中都存在一定的缺陷和不足。本文在前人研究的基础上,采用粗糙集与神经网络相结合的方法来分析股票价格运行趋势,研究的主要内容如下:(1)利用粗糙集对股票原始数据进行约简,并采用了一种基于量子计算与遗传算法相结合的属性约简方法。(2)通过对各种神经网络模型优缺点的比较,确定选用RBF网络模型。(3)通过Matlab仿真实验,详细地比较了自组织选取中心算法、有监督学习选取中心算法和正交最小二乘法选取中心算法三种学习算法应用在预测股价走势预测中的准确度。(4)建立了基于粗糙集和RBF网络的股价运行趋势的分析模型。将股票原始数据,经粗糙集约简处理之后,选择一种约简结果作为网络输入向量;并将网络输出向量走势图的拐点分成六类进行分析。(5)最后,本文选取了几个具有代表性的上市公司的数据验证了本模型的可信度和实用性。更多还原

【Abstract】 So far, people have been looking for all kinds of effective methods to avoid the risk of stock market and to obtain higher returns from stocks, so many technologies for pretending the trend of stock market have been produced. For the complexity of the stock market, many technologies have exposed their shortcomings and insufficiency.In this paper, the author bases on the previous studies and uses rough sets and neural network to predict the trend of stock price. The main contends are as follows:(1)At first, obtain the original data from stock market using Da Zhihui Software, and then use rough sets to extract representative data from original data. In this process, a new algorithm based on quantum computing and genetic algorithm has been used and has some advantages compared with other algorithms.(2)Considering the various advantages of RBF network, the author chooses it for establishing the pretending model.(3)As there are many learning methods for RBF network, so choosing the effective method has been the focus. In this paper, the author compares four algorithms and chooses the best from them through experiments.(4)At last, the author establishes a model based on the front research. The input vectors are choosed from the rough sets results; the output vector is divided six kind and drawned in the result chart to help users make decisions more effectively.Finally, the author uses representative data to validate the correctness and credibility of the model .更多还原