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Prediction of Total order amount based on BP Neural Network optimized by Genetic Algorithm

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DOI: 10.23977/meet.2019.93714


Hai Zhang, Shixin Li, Xiaoyu Liu

Corresponding Author

Shixin Li


Two forward neural networks were established in this study. Training and learning of factor data and prediction results were conducted respectively then the weights and thresholds of the two networks are optimized by genetic algorithm, finally the set of target values can still be predicted without factor data. In order to predict the total order amount of a training institution outside school, the genetic algorithm is used to optimize the BP neural network to establish an effective prediction model based on the analysis of the influence factors of the total order amount. This model not only has the strong learning ability of BP neural network, but also combines the excellent global searching ability of genetic algorithm. The innovation of this study is to use the network 1 training factor data to get the corresponding annual value, and then the network 2 training continuous 3 years’ factor data forecast results for the value of the fourth year. The whole process of solving the fourth year’s value does not need the factor data of this year.


Genetic Algorithm,Bp Neural Network,Total Order Forecast,Weight Threshold Optimization, Secondary Training And Learning

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