Approximate modeling for high order non-linear functions using small sample sets

Tung I. Tsai, Der Chiang Li

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


Having high learning accuracies, neural networks are widely applied for solving function approximation problems. Nevertheless, it is difficult to train a neural network to recognize a non-linear function using a small training sample set. Because the errors between real values and estimated values are significant and almost impossible to figure out using insufficient samples. This study develops an algorithm combining segmentation technique and artificial samples to overcome this situation. The strategy is to shorten the model range to minimize the total estimation error. Another supportive strategy known as artificial samples generation is also employed to fill information gaps. At the end of this research, the results of the computational examples indicate that learning accuracy can be significantly improved using the proposed method involving a very small data set.

Original languageEnglish
Pages (from-to)564-569
Number of pages6
JournalExpert Systems With Applications
Issue number1
Publication statusPublished - 2008 Jan 1

All Science Journal Classification (ASJC) codes

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


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