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

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
Dragan Kukolj, Emil Levi

Abstract

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.

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Citations

Oct 15, 2010·International Journal of Neural Systems·Petia Koprinkova-Hristova
Aug 9, 2013·International Journal of Neural Systems·Yiannis BoutalisDimitrios Theodoridis
Dec 18, 2015·Biological cybernetics·I ChihiM Benrejeb
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Sep 1, 2005·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Chen-Sen OuyangShie-Jue Lee
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Sep 1, 2015·IEEE Transactions on Cybernetics·Ching-An ChengChih-Chun Cheng
Dec 17, 2015·IEEE Transactions on Cybernetics·Ching-An Cheng, Han-Pang Huang
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Feb 11, 2016·IEEE Transactions on Neural Networks and Learning Systems·Zhao LuKenneth Butts
Sep 18, 2020·International Journal of Neural Systems·João Angelo Ferres BroginDouglas Domingues Bueno

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