摘要： GMDH 是一种具有自组织特征的数据处理方法，适用于非线性系统的建模，股指是一种重要的金融数据，具有混沌特性。该文将相空间重构引入了GMDH 神经网络的建模中，并将之应用于道琼斯等股指的预测。同BP 神经网络方法及一阶局域预测法相比，GMDH获得了更好的预测效果。
关键词:
成组数据处理的神经网络算法；股指；预测
Abstract: The group method of data handling is a self-organizing data handling method, it is very apt to model non-linear system. The paper applies the revised GMDH method in prediction of the stock indexs which has chaotic character, comparing with the BackPropagation(BP) and the local prediction method of chaotic series with order one, it shows better results
Key words:
Group method of data handling (GMDH); Stock index; Prediction
王光强，周佩玲. 神经网络算法在股指预测中的应用[J]. 计算机工程, 2006, 32(1): 211-212.
WANG Guangqiang, ZHOU Peiling. Application of Neuron Network in Stock Index Predicton[J]. Computer Engineering, 2006, 32(1): 211-212.