期刊
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
卷 34, 期 1, 页码 272-282出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2003.811119
关键词
competitive neural network; fuzzy model; process industry modeling; speed estimation of motor drives; system identification
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据