JRM Vol.12 No.6 pp. 682-688
doi: 10.20965/jrm.2000.p0682


Knowledge Acquisition by Improved Fuzzy ID3 Algorithm and Stability Analysis for Jacket Tank Temperature Control

Masaki Arao*, Masahito Tanaka** and Shigeyasu Kawaji**

*Information Technology Research Center, OMRON Corporation, Shimokaiinji, Ngaokakyo-City, Kyoto 617-8510, Japan

**Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan

September 20, 2000
October 8, 2000
December 20, 2000
Knowledge acquisition, Improved fuzzy ID3 algorithm, Jacket tank, Stability analysis
The extraction of knowledge from operation data is an important theme in an autonomous control system. An efficient method for making a decision tree for classification from data is the fuzzy ID3 algorithm using fuzzy sets. However, the definition of fuzzy sets greatly affects the generation of fuzzy trees. In this paper, we propose a new version of fuzzy ID3 algorithms to generate a fuzzy decision maximizing the expected value of transferred information by applying a random search method for determining the fuzzy set, and by using the improved fuzzy ID3 algorithm an automatic extraction of control knowledge from operational data by skilled operator of a jacket tank process. As a result, a fuzzy controller that can decide output of a control switch from both tank temperature error and differentiation of error is designed. Further, a method of analyzing the stability of the fuzzy control system by the modified fuzzy ID3 algorithm is proposed using the phase planes of tank temperature.
Cite this article as:
M. Arao, M. Tanaka, and S. Kawaji, “Knowledge Acquisition by Improved Fuzzy ID3 Algorithm and Stability Analysis for Jacket Tank Temperature Control,” J. Robot. Mechatron., Vol.12 No.6, pp. 682-688, 2000.
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Last updated on Jun. 03, 2024