Adaptive Least Trimmed Squares Fuzzy Neural Network

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, we propose the adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies the scale estimate to the least trimmed squares fuzzy neural network (LTS-FNN). The emphasis of this paper is particular on the robustness against the outliers and the choice of the trimming constant can be determined adaptively. Some numerical examples will be provided to compare the robustness against outliers for usual FNN and the ALTS-FNN. Simulation results show that the ALTS-FNN in the paper have good performance for outlier detection.
Original languageEnglish
Title of host publication2012 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY2012)
PublisherIEEE
Pages413-416
Number of pages4
ISBN (Print)978-1-4673-2056-6
DOIs
StatePublished - 2012

Keywords

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

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