LDHA-BP在大气PM2.5浓度预测中的应用

Jianqiang Zhao, J. Dou, Kao Ge
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引用次数: 4

摘要

LDHA-BP算法是针对BP神经网络预测大气PM2.5浓度的不足而设计的。该算法通过引入Leader Dolphins Herd algorithm (LDHA),将求解BP神经网络最优权值和阈值的过程转化为寻找海豚捕食者最优位置的过程。该算法有效地结合了BP神经网络良好的泛化能力和LDHA的全局优化能力、局部搜索能力。武汉某监测点PM2.5检测数据预测未来PM2.5浓度。实验结果表明,LDHA-BP预测PM2.5浓度更快、更准确,具有较好的实用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of LDHA-BP in Prediction of Atmospheric PM2.5 Concentration
LDHA-BP algorithm is designed for lack of using BP neural networks to predict atmospheric concentrations of PM2.5. In this algorithm, the process of solving the optimal weights and thresholds of BP neural networks will be transformed into the process of finding the optimal location of the dolphin species predator's, by introducing Leader Dolphins Herd Algorithm (LDHA).The algorithm effectively combines the good generalization ability of BP neural network neural and the global optimization ability, local search capability of LDHA. Test data for PM2.5 from a monitoring point in Wuhan to predict the PM2.5 concentrations in future. The experimental results show that LDHA-BP is faster, more accurate to predict the PM2.5 concentrations and it has good practical value.
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