A neural network weight determination model designed uniquely for small data set learning

Der Chiang Li, Chiao Wen Liu

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Environment characteristics are dynamic and changeable. In customized or flexible manufacturing systems, the collected data used for analysis is often small. There are many studies on small data set problems. However, most papers attack the problem by developing data pre-treatment methods which normally require abstruse mathematical knowledge, deterring engineers from applying the methods in practice. This paper develops a unique neural network to accurately predict small data sets. This neural network is developed based on the concept of the data central location tracking method (CLTM) to determine net weights as the learning rules. It not only makes accurate forecasts using small data sets but it also facilitates knowledge learning for engineers.

Original languageEnglish
Pages (from-to)9853-9858
Number of pages6
JournalExpert Systems With Applications
Volume36
Issue number6
DOIs
Publication statusPublished - 2009 Aug 1

All Science Journal Classification (ASJC) codes

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

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