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

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 34; no. 1; pp. 272 - 282
• Main Authors: Kukolj, D., Levi, E.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2004
• Subjects: Character generation; Clustering algorithms; Electrical equipment industry; Fuzzy logic; Fuzzy systems; Genetic algorithms; Humans; Neural networks; Optimization methods; Recurrent neural networks
• ISSN: 1083-4419; 1941-0492
• DOI: 10.1109/TSMCB.2003.811119

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.