Automatic generation of fuzzy control rule by machine learning methods
Resource
Robotics and Automation, 1995. Proceedings., 1995 IEEE International Conference on
Journal
Robotics and Automation, 1995. Proceedings., 1995 IEEE International Conference on
Pages
-
Date Issued
1995-05
Date
1995-05
Author(s)
DOI
N/A
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.
SDGs
Type
conference paper
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