CAJ | 학술논문

目前备件需求预测的研究在历史数据的选取和预测方法上存在诸多不合理,如缺少数据预处理及与忽视数据与设备的特性之间的关系,需要给予解决.在考虑不同备件之间需求相关性进行预处理的基础上,以某型大型空气压缩机为例,利用BP神经网络方法,对其备件历史需求数量的时间序列数据建立预测模型.最后将预处理后的历史数据输入到神经网络预测模型之中,并将模型的预测结果与未考虑备件之间需求相关性的预测结果进行比较,可以有效解决神经网络的"欠训练"问题,平均偏差率显著降低.
목전비건수구예측적연구재역사수거적선취화예측방법상존재제다불합리,여결소수거예처리급여홀시수거여설비적특성지간적관계,수요급여해결.재고필불동비건지간수구상관성진행예처리적기출상,이모형대형공기압축궤위례,이용BP신경망락방법,대기비건역사수구수량적시간서렬수거건립예측모형.최후장예처리후적역사수거수입도신경망락예측모형지중,병장모형적예측결과여미고필비건지간수구상관성적예측결과진행비교,가이유효해결신경망락적"흠훈련"문제,평균편차솔현저강저.
At present, there are some mistakes in choice, pretreatment and forecasting of time series datum of spare parts demand in some researches, such as improper data set. using raw datum indiscriminately and ignoring the relationship between the datum and the equipments' characters, which need to be improved. Taking the large-size air compressor as an example, its spare parts historical demand data series were pretreated. Based on this a forecast model of time series demand of spare parts was presented with BP neural networks. In the end. the processed demand time series datum were input into neural networks forecasting model. The forecasting results between raw datum and processed datum, which were using neural networks, were compared. The phenomena of "lack-training" vanished, and the average deviation rate remarkably reduced.