A simple method for identification of singleton fuzzy models
Resource
International Journal of Systems Science 36 (13): 845-854
Journal
International Journal of Systems Science
Journal Volume
36
Journal Issue
13
Pages
845-854
Date Issued
2005
Date
2005
Author(s)
Abstract
Abstract This article presents a simple method for constructing a singleton fuzzy model from a given set of input/output data. The method consists of three computational steps: the initial phase, the growth phase, and the optional refining phase. The universe of discourse and two linguistic terms for each input variable and a rule base are established during the initial phase. Additional linguistic terms and rules are then appended sequentially during the growth phase to modify the model structure and to elevate the performance. During the optional refining phase the overall modelling performance can be further improved by adjusting the singleton outputs of the rule set in the sense of least squares. The proposed identification method can simultaneously provide an appropriate model structure and parameters without any time-consuming optimisation. Several numerical examples demonstrate the effectiveness of the proposed identification method. Keywords: Fuzzy setsFuzzy modelIndentification Acknowledgement The authors would like to thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC87-2214-E-002-014. Cheng-Liang Chen received the Ph.D. degree in chemical engineering from National Taiwan University in 1987. He is currently a Professor with the Department of Chemical Engineering, National Taiwan University. His research interests include neural/fuzzy modeling and control, identification and control systems design, process integration and optimization, production scheduling and supply chain management. Shuo-Huan Hsu received the Master degree in chemical engineering from National Taiwan University in 1998. Currently, he is a Ph.D. candidate at the Department of Chemical Engineering, Purdue University, USA. His research interests include system identification, genetic algorithms, system biology. Chung-Tyan Hsieh received the Ph.D. degree in chemical engineering from National Taiwan University in 1997. His research interests include expert systems, fuzzy modeling and control. In addition to the research work, Dr. Hsieh has developed a Chinese TeX system which is one of the main typesetting systems used in the Republic of China. Tzu-Chi Wang received the Ph.D. degree in chemical engineering from National Taiwan University in 2001. Currently, he is a research fellow at the Center for Environmental, Safety and Health, the Industrial Technology Research Institute, Taiwan. His research interests include control systems design, process monitoring and safety engineering.
Type
journal article
File(s)![Thumbnail Image]()
Loading...
Name
13.pdf
Size
3.39 MB
Format
Adobe PDF
Checksum
(MD5):5c75c70b1e8b79c3883238401216a2c5