Neural computing: theory and practice
Neural computing: theory and practice
Introduction to Grey system theory
The Journal of Grey System
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Sales forecasting using time series and neural networks
CIE '96 Proceedings of the 19th international conference on Computers and industrial engineering
Computers and Operations Research
The inventory management system for automobile spare parts in a central warehouse
Expert Systems with Applications: An International Journal
Classification of non-alcoholic beer based on aftertaste sensory evaluation by chemometric tools
Expert Systems with Applications: An International Journal
Review: Hybrid expert systems: A survey of current approaches and applications
Expert Systems with Applications: An International Journal
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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 required amount 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 like grey prediction method, back-propagation neural network (BPN), fuzzy neuron network (FNN), etc. 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.