mRMR和PSO算法对神经网络预测模型优化效果OA

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.