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- W2079628043 abstract "Accurate demand forecasting is an important precondition to carry out an active and detailed oriented equipment materials support. Learning and self-adaptive ability of BP-neural networks is used to learn the law of equipment demand, with genetic algorithm combined to improve the convergence speed of BP-neural networks. An optimized algorithm combining BP-neural networks and genetic algorithm is proposed for forecasting equipment materials demand. The simulation result shows that the proposed method owns high precision and fast convergence compared with original BP-neural networks." @default.
- W2079628043 created "2016-06-24" @default.
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- W2079628043 date "2015-01-01" @default.
- W2079628043 modified "2023-09-23" @default.
- W2079628043 title "Research on Equipment Materials Demand Forecast based on Genetic BP-Neural Networks" @default.
- W2079628043 cites W1530176896 @default.
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- W2079628043 doi "https://doi.org/10.2991/iiicec-15.2015.108" @default.
- W2079628043 hasPublicationYear "2015" @default.
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