基于柔性神经树模型的股票市场风险预测

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。

Forecasting stock market risks based on the flexible neural tree

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 neural tree model are optimized by probabilistic incremental program evolution and simulation annealing, respectively. It has been demonstrated that the method is very effective for forecasting stock market risk.