摘要
利用柔性神经树模型的改进结构优化算法对影响股票市场的过程参数进行筛选,在精确度较高的前提下在比较短的时间内找到影响股票市场风险的重要参数。在柔性神经树模型的学习过程中,该算法的进化代数不是一个固定值,而是以误差率来控制进化代数,试验证明此算法使模型最优,效率和精确度非常高。柔性神经树模型的结构和参数优化分别由概率增强式程序进化和模拟退火算法完成。研究结果表明该改进方法对预测股票市场风险是非常有效的。
The improved structural optimization algorithm of the flexible neural tree model is employed to select the parameters for effecting stock market production. With higher accuracy and shorter time, important parameters which affect the risk of the stock market are found. In the period of learning of the flexible neural tree model, the evolution generation of the algorithm is not a fixed value and the mean error rate is utilized to control the evolution generation. The structure and parameters of the flexible neu- ral tree model are optimized by probabihstic incremental program evolution and simulation annealing, respectively. It has been demonstrated that the method is very effective for forecasting stock market risk.
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2009年第11期44-47,共4页
Journal of Shandong University(Natural Science)
基金
国家自然科学基金资助项目(69902005)
山东省攻关计划资助项目(2008GG10001001)
关键词
股票市场
柔性神经树模型
误差率
概率增强式程序
模拟退火
stock market
flexible neural tree model
error rate
probabilistic incremental program evolution
simulation annealing
