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首页> 外文会议>IEEE International Conference on Cloud Computing and Big Data Analysis >Prediction of particulate matter concentration in Chengdu based on improved differential evolution algorithm and BP neural network model
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Prediction of particulate matter concentration in Chengdu based on improved differential evolution algorithm and BP neural network model

机译：基于改进的差分进化算法和BP神经网络模型的成都颗粒物浓度预测

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Based on the establishment of improved BP neural network model, this paper has carried on the research to the PM by collecting the particle concentration and the related meteorological data for prediction. In this paper, the improved difference evolution algorithm is used to optimize the weights and thresholds of the BP neural network model (IDE-BPNN) for the traditional BP neural network model, which is too dependent on the initial value, the convergence rate is slow and easy to fall into the local minimum. The improved model is compared with the traditional BP neural network model and the other five optimized BP neural network models. The root mean square error (RMSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE) are smaller than those of other models, and the index of agreement (IA) of IDE-BPNN is the highest, the mean bias error (MBE) of IDE-BPNN tends to be zero, and the IDE-BPNN model performs better.
机译：在建立改进的BP神经网络模型的基础上，通过收集颗粒物浓度和相关的气象数据进行预报，对颗粒物进行了研究。本文采用改进的差分进化算法来优化传统BP神经网络模型的BP神经网络模型（IDE-BPNN）的权重和阈值，该算法过于依赖初始值，收敛速度慢并容易陷入当地最低要求。将改进后的模型与传统的BP神经网络模型以及其他五个优化的BP神经网络模型进行了比较。均方根误差（RMSE），平均绝对误差（MAE）和平均绝对百分比误差（MAPE）小于其他模型的均方根误差，IDE-BPNN的一致性指数（IA）最高， IDE-BPNN的平均偏差误差（MBE）趋于为零，并且IDE-BPNN模型的性能更好。

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