Adam优化的BP神经网络地铁空调环境模式检测

Adam optimized BP neural network for subway air conditioning environment mode detection

针对目前地铁空调系统环境模式的检测判定,依旧存在效率低下智能化程度不高的问题,设计了Adam优化的BP神经网络地铁空调环境模式检测模型。选取3个关键变量:烟雾浓度、二氧化碳浓度、温度作为环境模式识别的特征条件,采用Adam优化算法对传统BP神经网络模型的梯度下降进行优化,采用一阶矩估计和二阶矩估计动态调整每个参数的学习率,加快模型学习,提高网络识别精度,并在收敛时减小震荡。实验结果表明,优化后的BP神经网络地铁环境模式检测模型收敛速度提高了98.88%,预测错误平均个数减少了45.6%,且收敛过程中震荡大大减小。同时相比于其他机器学习多分类模型,优化后的BP神经网络模型准确率为99.88%,检测运行时间为12 ms,整体性能更优。

In view of the current detection and judgment of the environmental mode of subway air conditioning system,there is still the problem of low efficiency and low intelligence degree.BP neural network optimized by Adam is designed to detect the environmental mode of subway air conditioning system.Choose three key variables:smoke concentration,carbon dioxide concentration,temperature,as a condition of environment characteristics of pattern recognition,Adam algorithm are used to optimize the gradient descent of the traditional BP neural network model,first-order moment estimation and second-order moment estimation are used to dynamically adjust the learning rate of each parameter,to speed up the model learning,improve the identification accuracy of the network,and reduce the oscillation during convergence.The experimental results show that the convergence speed of the optimized BP neural network subway environmental mode detection model is improved by 98.88%,the average number of prediction errors is reduced by 45.6%,and the oscillation is greatly reduced in the convergence process.At the same time,compared with other machine learning multi-classification models,the accuracy of the optimized BP neural network model is 99.88%,the detection running time is 12 ms,and the overall performance is better.