Abstract
This paper presents a multi-strategy learning technique for automatic generation of fuzzy control rules. In order to eliminate irrelevant input variables and to prioritize relevant ones according to their influences on the output value(s), the ID3 algorithm is adopted to classify the given set of training I/O data. The resulting decision tree can be easily converted into IF-THEN rules, which are then fuzzified. The fuzzy rules are further improved by tuning the parameters that define their membership functions using the gradient-descent approach. Experimental results of applying the proposed technique to nonlinear system identification have shown improvements over previous work in the area. In addition, it has been successfully applied to mobile robot control in unknown environments.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE International Conference on Robotics and Automation |
| Publisher | IEEE |
| Pages | 287-292 |
| Number of pages | 6 |
| Volume | 1 |
| Publication status | Published - 1995 |
| Externally published | Yes |
| Event | Proceedings of the 1995 IEEE International Conference on Robotics and Automation. Part 1 (of 3) - Nagoya, Jpn Duration: May 21 1995 → May 27 1995 |
Other
| Other | Proceedings of the 1995 IEEE International Conference on Robotics and Automation. Part 1 (of 3) |
|---|---|
| City | Nagoya, Jpn |
| Period | 5/21/95 → 5/27/95 |
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ASJC Scopus subject areas
- Software
- Control and Systems Engineering
Cite this
Automatic generation of fuzzy control rules by machine learning methods. / Hsu, Shih Chun; Hsu, Jane Yung jen; Chiang, I-Jen.
Proceedings - IEEE International Conference on Robotics and Automation. Vol. 1 IEEE, 1995. p. 287-292.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Automatic generation of fuzzy control rules by machine learning methods
AU - Hsu, Shih Chun
AU - Hsu, Jane Yung jen
AU - Chiang, I-Jen
PY - 1995
Y1 - 1995
N2 - This paper presents a multi-strategy learning technique for automatic generation of fuzzy control rules. In order to eliminate irrelevant input variables and to prioritize relevant ones according to their influences on the output value(s), the ID3 algorithm is adopted to classify the given set of training I/O data. The resulting decision tree can be easily converted into IF-THEN rules, which are then fuzzified. The fuzzy rules are further improved by tuning the parameters that define their membership functions using the gradient-descent approach. Experimental results of applying the proposed technique to nonlinear system identification have shown improvements over previous work in the area. In addition, it has been successfully applied to mobile robot control in unknown environments.
AB - This paper presents a multi-strategy learning technique for automatic generation of fuzzy control rules. In order to eliminate irrelevant input variables and to prioritize relevant ones according to their influences on the output value(s), the ID3 algorithm is adopted to classify the given set of training I/O data. The resulting decision tree can be easily converted into IF-THEN rules, which are then fuzzified. The fuzzy rules are further improved by tuning the parameters that define their membership functions using the gradient-descent approach. Experimental results of applying the proposed technique to nonlinear system identification have shown improvements over previous work in the area. In addition, it has been successfully applied to mobile robot control in unknown environments.
UR - http://www.scopus.com/inward/record.url?scp=0029182129&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029182129&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0029182129
VL - 1
SP - 287
EP - 292
BT - Proceedings - IEEE International Conference on Robotics and Automation
PB - IEEE
ER -