A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines

Zhan Li Sun, Kin Fan Au, Tsan Ming Choi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

121 Citations (Scopus)

Abstract

This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are processed are grouped by the k-means clustering method. The membership of arbitrary input for each fuzzy rule is then derived through an ELM, followed by a normalization method. At the same time, the consequent part of the fuzzy rules is obtained by multiple ELMs. At last, the approximate prediction value is determined by a weight computation scheme. For the ELM-based TSK fuzzy inference system, two extensions are also proposed to improve its accuracy. The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. Moreover, the proposed methods have a competitive performance in training time and accuracy compared to three ANFIS methods.
Original languageEnglish
Pages (from-to)1321-1331
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume37
Issue number5
DOIs
Publication statusPublished - 1 Oct 2007

Keywords

  • κ-means clustering
  • Adaptive neuro-fuzzy inference system (ANFIS)
  • Extreme learning machine (ELM)
  • Takagi-Sugeno-Kang (TSK) fuzzy inference system

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Medicine(all)
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this