Study on Adaptive Least Trimmed Squares Fuzzy Neural Network

Shih-Hui Liao, Ming-Feng Chang, Jyh-Yeong Chang, Chin-Teng Lin

Research output: Contribution to journalArticlepeer-review

Abstract

In the largest samplings of data, outliers are observations that are well separated from the major samples. To deal with outlier problems, a least trimmed squares (LTS) estimator is developed for robust linear regression problems. It is meaningful to generalize the LTS estimator to fuzzy neural network (FNN) for robust nonlinear regression problems. In addition, the determination of the trimming constant is important when using the LTS estimator. In this paper, we propose the use of an adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies a scale estimate to a LTS-FNN. This paper particularly emphasizes the robustness of the proposed network against outliers and an automatic determination of the trimming percentage. Simulation problems are provided to compare the performance of the proposed ALTS-FNN, with an LTS-FNN and typical FNN. Simulation results show that the proposed ALTS-FNN is highly robust against outliers.
Original languageEnglish
Pages (from-to)338-346
Number of pages9
JournalInternational Journal of Fuzzy Systems
Volume15
Issue number3
StatePublished - Sep 2013

Keywords

  • least trimmed squares (LTS) estimator
  • fuzzy neural network
  • least trimmed squares fuzzy neural network
  • LTS-FNN
  • adaptive least trimmed squares fuzzy neural network
  • ALTS-FNN

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