煤自然发火期的主成分神经网络预测模型

煤自然发火期是衡量煤自燃特性的一个重要参数，也是指导井下防灭火工作的重要参考依据。结合主成分分析与神经网络的优点，提出了基于主成分分析的神经网络煤自然发火期预测模型。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。

Principal component neural network prediction model for coal self-ignition duration

The coal self-ignition duration is an important parameter not only to measure the characteristic of coal self-ignition but also to direct the fire control technique in coal mines.The BP neural network prediction model for the self-ignition duration is established based on the Principal Component Analysis(PCA).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...