Title page for etd-0623108-234132


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URN etd-0623108-234132
Author Tzung-Han Wu
Author's Email Address jasonki00@yahoo.com.tw
Statistics This thesis had been viewed 5347 times. Download 1402 times.
Department Electrical Engineering
Year 2007
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Study on Ramsay Fuzzy Neural Networks
Date of Defense 2008-06-13
Page Count 62
Keyword
  • M-estimators
  • Fuzzy Neural Networks
  • robust regression
  • Ramsay
  • Abstract In this thesis, M-estimators with Ramsay‚Äôs  function used in robust regression theory for linear parametric regression problems will be generalized to nonparametric Ramsay fuzzy neural networks (RFNNs) for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers. This provides alternative learning machines when faced with general nonlinear learning problems. Simple weight updating rules based on incremental gradient descent and iteratively reweighted least squares (IRLS) will be derived. Some numerical examples will be provided to compare the robustness against outliers for usual fuzzy neural networks (FNNs) and the proposed RFNNs. Simulation results show that the RFNNs proposed in this thesis have good robustness against outliers.
    Advisory Committee
  • Tsu-Tian Lee - chair
  • Jeng-Yih Juang - co-chair
  • Chang-Hua Lien - co-chair
  • Jyh-Horng Jeng - co-chair
  • Jer-Guang Hsieh - advisor
  • Files
  • etd-0623108-234132.pdf
  • indicate access worldwide
    Date of Submission 2008-06-23

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