Robust fuzzy clustering neural network based on ε-insensitive loss function

Shitong Wang, Fu Lai Korris Chung, Deng Zhaohong, Hu Dewen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)


In the paper, as an improvement of fuzzy clustering neural network FCNN proposed by Zhang et al., a novel robust fuzzy clustering neural network RFCNN is presented to cope with the sensitive issue of clustering when outliers exist. This new algorithm is based on Vapnik's ε-insensitive loss function and quadratic programming optimization. Our experimental results demonstrate that RFCNN has much better robustness for outliers than FCNN.
Original languageEnglish
Pages (from-to)577-584
Number of pages8
JournalApplied Soft Computing Journal
Issue number2
Publication statusPublished - 1 Mar 2007


  • ε-Insensitive loss function
  • Fuzzy clustering
  • Neural networks
  • Outliers
  • Robustness

ASJC Scopus subject areas

  • Software


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