Research on Equipment Materials Demand Forecast based on Genetic BP-Neural Networks

摘要

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.