title: Identification of complex systems based on neural and Takagi-Sugeno fuzzy model.
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.
authors.affiliation: Faculty of Engineering, University of Novi Sad, Novi Sad 21000, Serbia.
authors.forename: Dragan
authors.initials: D
authors.lastname: Kukolj
authors.forename: Emil
authors.initials: E
authors.lastname: Levi