Air spares support is general term of using and repairing of aircrafts which is the material foundation of aero technical support, its effectiveness influences operational effectiveness and equipments of aircrafts dir...Air spares support is general term of using and repairing of aircrafts which is the material foundation of aero technical support, its effectiveness influences operational effectiveness and equipments of aircrafts directly. Based on particle swarm optimization algorithm, a new model is proposed to optimize the distribution of the cost of air spares, it take the funds as resource and the improvement of performance efficiency as objective and deduces the expressions to get the best distribution plan. The results of experiments indicate that this model can make full use of the limited funds and obtain the highest efficiency of air spares support.展开更多
针对空调为二次泵变流量系统时,考虑分区域供冷工况下,采用多目标回归方式解决负荷预测问题将有利于提高负荷预测准确性的情况,提出了两种多目标回归的中央空调负荷预测模型,即多目标支持向量回归(support vector regression,SVR)负荷...针对空调为二次泵变流量系统时,考虑分区域供冷工况下,采用多目标回归方式解决负荷预测问题将有利于提高负荷预测准确性的情况,提出了两种多目标回归的中央空调负荷预测模型,即多目标支持向量回归(support vector regression,SVR)负荷预测模型和多目标长短期记忆(long short term memory,LSTM)神经网络负荷预测模型,利用上海市某医院的二次泵变流量系统数据对两个模型进行训练和预测,并与单目标回归预测模型进行比较.研究结果表明:相较单目标回归预测模型,两种多目标预测模型的预测精度更高;多目标SVR负荷预测模型较多目标LSTM负荷预测模型的预测准确性更高.展开更多
文摘Air spares support is general term of using and repairing of aircrafts which is the material foundation of aero technical support, its effectiveness influences operational effectiveness and equipments of aircrafts directly. Based on particle swarm optimization algorithm, a new model is proposed to optimize the distribution of the cost of air spares, it take the funds as resource and the improvement of performance efficiency as objective and deduces the expressions to get the best distribution plan. The results of experiments indicate that this model can make full use of the limited funds and obtain the highest efficiency of air spares support.
文摘针对空调为二次泵变流量系统时,考虑分区域供冷工况下,采用多目标回归方式解决负荷预测问题将有利于提高负荷预测准确性的情况,提出了两种多目标回归的中央空调负荷预测模型,即多目标支持向量回归(support vector regression,SVR)负荷预测模型和多目标长短期记忆(long short term memory,LSTM)神经网络负荷预测模型,利用上海市某医院的二次泵变流量系统数据对两个模型进行训练和预测,并与单目标回归预测模型进行比较.研究结果表明:相较单目标回归预测模型,两种多目标预测模型的预测精度更高;多目标SVR负荷预测模型较多目标LSTM负荷预测模型的预测准确性更高.