Optimization Effect of mRMR and PSO Algorithms on Neural Network Prediction Models
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.
国家科技期刊平台
NSTL
万方数据
维普
