General Aircraft Material Demand Forecast Based on Modified PSO Optimized BP Neural Network
نویسندگان
چکیده
Air material demand forecast is an important content of air material management, How to scientifically determine the general aircraft material demand has always been a key research subject of general aviation enterprise. Consider the general aircraft material demand forecast problem, forecast method is proposed by using Modified Particle Swarm Optimization (MPSO) algorithm to optimize the BP neural network. Firstly analyzed the main influence factors of general aircraft material demand, then introduces the basic principle of BP neural network and PSO algorithm and its improvement and MPSO-BP neural network forecast model is constructed, finally case analysis is carried out by using historical data of general aviation enterprise. The results show that the model prediction accuracy is improved effectively and good results have been achieved.
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