T2fela: Type-2 fuzzy extreme learning algorithm for fast training of interval type-2 TSK fuzzy logic system

Zhaohong Deng, Kup Sze Choi, Longbing Cao, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

58 Citations (Scopus)


A challenge in modeling type-2 fuzzy logic systems is the development of efficient learning algorithms to cope with the ever increasing size of real-world data sets. In this paper, the extreme learning strategy is introduced to develop a fast training algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy logic systems. The proposed algorithm, called type-2 fuzzy extreme learning algorithm (T2FELA), has two distinctive characteristics. First, the parameters of the antecedents are randomly generated and parameters of the consequents are obtained by a fast learning method according to the extreme learning mechanism. In addition, because the obtained parameters are optimal in the sense of minimizing the norm, the resulting fuzzy systems exhibit better generalization performance. The experimental results clearly demonstrate that the training speed of the proposed T2FELA algorithm is superior to that of the existing state-of-the-art algorithms. The proposed algorithm also shows competitive performance in generalization abilities.
Original languageEnglish
Article number6603357
Pages (from-to)664-676
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number4
Publication statusPublished - 1 Jan 2014


  • Extreme learning
  • Fast training
  • Parameter optimization
  • Type-2 fuzzy logic system (T2FLS)

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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