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鉴于矿山设备产量具有灰色和不确定性的特征,本文利用矿山设备产量的历史数据,建立了基于灰色和BP神经网络的组合预测模型。组合预测模型中各单一模型的权系数通过熵值法确定,克服了传统权系数确定方法的主观性,使得组合预测方法更具客观性。最后,实例验证了所构建的组合模型较传统的单一预测模型有良好的预测效果。
In view of the gray and uncertainty characteristics of mine equipment production, this paper establishes a combined forecasting model based on gray and BP neural network using the historical data of mine equipment production. The weight coefficient of each single model in the combined forecasting model is determined by the entropy method, which overcomes the subjectivity of the traditional weighting coefficient determination method and makes the combined forecasting method more objective. Finally, the example verifies that the combined model has a good prediction effect compared with the traditional single prediction model.