计算机应用研究2011，Vol.28Issue(8)：2840-2843,4.DOI:10.3969/j.issn.1001-3695.2011.08.011
基于结构修剪神经网络的股票指数预测模型
Pruning structure neural network model for stock index forecasting
摘要
Abstract
Since stock market is a nonlinear system with internal structural complexity and external factors variability, proposed forecasting index system which involved Shanghai Composite Index' s price, volume, and macroeconomic indicators closely related to stock market. Then analyzed the long-run equilibrium and causal relationship among the variables. Based on Bayesian theory, in order to optimize structure and ensure generalization ability of network, added network complexity function to error function which could delete insensitive hidden layer neurons through dynamic adjusting penalty factor. The empirical results with different forecasting index systems indicate that the pruning structure neural network model based on BP algorithm can be an effective way to improve forecasting accuracy. Comparing with other neural network models, the proposed model can improve forecasting performance with higher forecasting accuracy and more concise structure.关键词
股票指数预测/预测指标体系/BP算法/贝叶斯分析/网络结构修剪Key words
stock index forecasting/ forecasting index system/ BP algorithm/ Bayesian analysis/ network structure pruning分类
信息技术与安全科学引用本文复制引用
孙彬,李铁克,张文学..基于结构修剪神经网络的股票指数预测模型[J].计算机应用研究,2011,28(8):2840-2843,4.基金项目
北京科技大学博士研究生科研基金资助项目 （）
