Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems

Tung I. Tsai, Der Chiang Li

研究成果: Article同行評審

38 引文 斯高帕斯(Scopus)

摘要

If the production process, production equipment, or material changes, it becomes necessary to execute pilot runs before mass production in manufacturing systems. Using the limited data obtained from pilot runs to shorten the lead time to predict future production is this worthy of study. Although, artificial neural networks are widely utilized to extract management knowledge from acquired data, sufficient training data is the fundamental assumption. Unfortunately, this is often not achievable for pilot runs because there are few data obtained during trial stages and theoretically this means that the knowledge obtained is fragile. The purpose of this research is to utilize bootstrap to generate virtual samples to fill the information gaps of sparse data. The results of this research indicate that the prediction error rate can be significantly decreased by applying the proposed method to a very small data set.

原文English
頁(從 - 到)1293-1300
頁數8
期刊Expert Systems With Applications
35
發行號3
DOIs
出版狀態Published - 2008 10月

All Science Journal Classification (ASJC) codes

  • 工程 (全部)
  • 電腦科學應用
  • 人工智慧

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