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

Tung I. Tsai, Der Chiang Li

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1293-1300
Number of pages8
JournalExpert Systems With Applications
Issue number3
Publication statusPublished - 2008 Oct

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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