粗集与神经网络相结合的股票价格预测模型

粗集和神经网络结合反映了人类智能的定性和定量、清晰和隐含、串行和并行相互交叉混合的常规思维机理。本文建立这样一种混合杂交模型用于股票价格波动趋势的预测,通过粗集对数据的二维约简预处理消除了样本中的噪声和冗余,在提高神经网络预测精度的同时降低了学习负担。为了获得最优的预测精度,本文还利用遗传算法进行属性离散化和网络学习。通过对上证综指的实证研究表明,这种混合杂交模型的性能明显优于BP和GA神经网络模型。

Rough Set Integrated Neural Network Model For Forecasting Stock Price

Rough set integrated neural network method reflects the human’s normal thinking mechanism which mixes the method of quantitative and qualitative,clear and uncertain,serial and parallel.This paper bulids such a model which using rough set’s 2 dimenation reduction ability to reduce the noise and redundence in the samples.So it improves the neural network’s forecasting accuracy as well as reducing its burden of learning.GA is also used in this paper to the attribute’s discretion and neural network learning to find the optimized forecasting accuracy.Case study shows the hybrid model is more competitive than the similar neural network model.