Logistics demand prediction of daily consumer goods based on GA-ACO-BP neural network
In response to the logistics demand of daily consumer goods,the grey correlation analysis method was used to calculate and sort the grey correlation degree of influencing factors from six aspects,namely,macroeconomic development level,relevant industry level,consumption capacity,logistics supply capacity,internet development level and trade level,and a prediction index system was constructed.Considering the small number of logistics-related data samples and the nonlinearity between their influencing factors,a daily consumer goods logistics demand prediction model based on the genetic algorithm-ant colony optimization-back propagation neural network(GA-ACO-BP neural network)was constructed by combining the global optimization ability of the genetic algorithm and the parallel computing ability of the ant colony algorithm.The GA-ACO-BP model,the GA-BP model and the BP model were used to predict the logistics demand of daily consumer goods nationwide,respectively.The results show that the GA-ACO-BP model can better fit the changes in logistics demand for daily consumer goods,with high prediction accuracy,and provides a model reference for logistics demand prediction research,which has certain practical value.
BP neural networkgenetic algorithmant colony algorithmdaily consumer goodslogistics demand
万方数据
维普
