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Identification of complex systems based on neural and Takagi-Sugeno fuzzy model

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2 Author(s)
Kukolj, D. ; Fac. of Eng., Univ. of Novi Sad, Serbia ; Levi, E.

The paper describes a neuro-fuzzy identification approach, which uses numerical data as a starting point. The proposed method generates a Takagi-Sugeno fuzzy model, characterized with transparency, high accuracy and a small number of rules. The process of self-organizing of the identification model consists of three phases: clustering of the input-output space using a self-organized neural network; determination of the parameters of the consequent part of a rule from over-determined batch least-squares formulation of the problem, using singular value decomposition algorithm; and on-line adaptation of these parameters using recursive least-squares method. The verification of the proposed identification approach is provided using four different problems: two benchmark identification problems, speed estimation for a DC motor drive, and estimation of the temperature in a tunnel furnace for clay baking.

Published in:

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:34 ,  Issue: 1 )

Date of Publication:

Feb. 2004

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