Applying Moving back-propagation neural network and Moving fuzzy-neuron network to predict the requirement of critical spare parts

作者：Chen Fei Long; Chen Yun Chin^*; Kuo Jun Yuan

来源：Expert Systems with Applications, 2010, 37(9): 6695-6704.

DOI：10.1016/j.eswa.2010.04.037

• Moving back-propagation neural network
• Moving fuzzy-neuron network
• Critical spare part
• Prediction

The critical spare parts (CSP) are vital to machine operation, which also have the characteristic of more expensive, larger demand variation, longer purchasing lead time than non-critical spare parts. Therefore, it is an urgent issue to devise a way to forecast the future requirement of CSP accurately.
This investigation proposed Moving back-propagation neural network (MBPN) and Moving fuzzy-neuron network (MFNN) to effectively predict the CSP requirement so as to provide as a reference of spare parts control. This investigation also compare prediction accuracy with other forecasting methods, such as grey prediction method, back-propagation neural network (BPN), fuzzy-neuron networks (FNN). All of the prediction methods evaluated the real data, which are provided by famous wafer testing factories in Taiwan, the effectiveness of the proposed methods is demonstrated through a real case study.

• 出版日期2010-9
• 单位清华大学