Aiming at the PM2.5 concentration in the air, which is affected by meteorological factors and atmospheric pollutants, and has the characteristics of nonlinearity and uncertainty, a prediction method of LMBP neural network based on Harris Hawk optimization algorithm is proposed. In the process of LMBP neural network weight threshold optimization process, Harris Hawk optimization algorithm (HHO) is introduced, and a LMBP initial weights and thresholds optimization method based on HHO algorithm is designed. This method utilizes the global optimization ability of HHO algorithm and effectively avoids the LMBP neural network is trapped in a local minimum worth of possibilities. The simulation results show that the prediction model based on the HHO-LMBP algorithm has higher accuracy and better stability than the DELMBP and LMBP algorithms.
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