基于主成分——BP神经网络的期货市场预测

Principal Component Analysis and BP Neural Network Modeling for Futures Foresting

结合主成分分析法和神经网络的优点,提出了基于主成分分析的神经网络方法来对期货市场进行预测.引入主成分分析法对原始输入变量进行预处理,选择输入变量的主成分作为网络输入,一方面减少了输入维度,消除了各输入变量的相关性;另一方面提高了网络的收敛性和稳定性,也简化了网络的结构.通过实例验证,基于主成分的神经网络比一般神经网络训练精度更高.

Forecasting the trends of the futures＇price bases on the principal component analysis and BP neural network modeling, inducting principal component analysis （PCA） to pre-analyze the original multi-objective variables, and using the principal components of original variables as the input of network can cut down the dimensions of input, and at the same time eliminates the relativity between variables, so improves the convergence speed and stability of network and simplifies network structur. And testing actural instances to validate that the performance of the PCA-BP neural network is better than normal neural network.