主成分分析与BP神经网络在煤耗氧速度预测中的应用

Application of principal component analysis and BP neural network in the rate of coal oxygen consumption prediction

结合主成分分析与神经网络的优点,提出了主成分分析与神经网络相结合的煤耗氧速度预测模型.采用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神经网络输入,一方面减少了输入变量的维数,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,同时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,学习时间更短,预测效果更优.

The prediction model for the rate of coal oxygen consumption was established based on the principal component analysis （PCA） and BP neural network method. Inducting 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 structure. Testing actual instances to validate that the PCA - BP neural network compared with the normal neural network improves the precision, reduces training time and possesses better performance.