煤气与热力2024，Vol.44Issue(1)：6-9,34,5.
mRMR和PSO算法对神经网络预测模型优化效果
Optimization Effect of mRMR and PSO Algorithms on Neural Network Prediction Models
杜润琪 1于丹 1刘益民 2岑悦2
作者信息
- 1. 北京建筑大学,北京 102627
- 2. 中国建筑科学研究院有限公司,北京 100013
- 折叠
摘要
Abstract
It is proposed to use the maximum relevance minimum redundancy(mRMR)algorithm and the particle swarm optimization(PSO)algorithm to optimize the BP neural network prediction model.The heating load of a residential building is predicted,and the prediction effects of three neural network prediction models(BP neural network prediction model,mRMR-BP neural network prediction model,and PSO-mRMR-BP neural network prediction model)are evaluated.Among the three neural network prediction models,the BP neural network prediction model has the worst prediction effect,and the PSO-mRMR-BP neural network prediction model has the best prediction effect.Compared with the BP neural network prediction model,through the mRMR algorithm to screen input variables and the PSO algorithm to opti-mize the initial parameters,the prediction effect of the PSO-mRMR-BP neural network prediction model is sig-nificantly improved.关键词
供热负荷/预测/BP神经网络/mRMR算法/PSO算法Key words
heating load/prediction/BP neural network/mRMR algorithm/PSO algorithm分类
信息技术与安全科学引用本文复制引用
杜润琪,于丹,刘益民,岑悦..mRMR和PSO算法对神经网络预测模型优化效果[J].煤气与热力,2024,44(1):6-9,34,5.
