Building a fuzzy model with transparent membership functions through constrained evolutionary optimization

In this paper, a new evolutionary scheme to design a TSK fuzzy model from relevant data is proposed. The identification of the antecedent rule parameters is performed via the evolutionary algorithm with the unique fitness function and the various evolutionary operators, while the identification of the consequent parameters is done using the least square method. The occurrence of the multiple overlapping membership functions, which is a typical feature of unconstrained optimization, is resolved with the help of the proposed fitness function. The proposed algorithm can generate a fuzzy model with transparent membership functions. Through simulations on various problems, the proposed algorithm found a TSK fuzzy model with better accuracy than those found in previous works with transparent partition of input space.
Publisher
INST CONTROL ROBOTICS & SYSTEMS
Issue Date
2004-09
Language
ENG
Keywords

SYSTEMS; INTERPRETABILITY; COMPLEXITY

Citation

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.2, pp.298 - 309

ISSN
1598-6446
URI
http://hdl.handle.net/10203/8422
Appears in Collection
EE-Journal Papers(저널논문)
  • Hit : 274
  • Download : 0
  • Cited 0 times in thomson ci
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡClick to seewebofscience_button
⊙ Cited 9 items in WoSClick to see citing articles inrecords_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0