Fuzzy-based classifier design for determining the eye movement data as an input reference in wheelchair motion control
Fuzzy logic is widely used in many complex and nonlinear systems for control, system identification and pattern recognition problems. The fuzzy logic controller provides an alternative to the PID controller which is a good tool for control of systems that are difficult to model. In this paper, the f...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Teknologi Malaysia
2015
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| Online Access: | http://irep.iium.edu.my/45037/ http://irep.iium.edu.my/45037/ http://irep.iium.edu.my/45037/1/5627-15766-1-SM.pdf |
| Summary: | Fuzzy logic is widely used in many complex and nonlinear systems for control, system identification and pattern recognition problems. The fuzzy logic controller provides an alternative to the PID controller which is a good tool for control of systems that are difficult to model. In this paper, the fuzzy-based classifiers were designed in order to determine the eye movement data. These data were used as an input reference in wheelchair motion control.
Then, a set of an appropriate fuzzy classification (FC) was designed based on the numerical data from eye movement data acquisitions that obtained from the electrooculogram (EOG) technique. Each fuzzy rule (FR) for this system is based on the form of IF-THEN rule. Since membership functions (MFs) are generated automatically, the proposed fuzzy learning algorithm can be viewed as a knowledge acquisition tool for classification problems. The experimental results on eye movement data were presented to demonstrate the contribution of the proposed approach for generating MFs using MATLAB simulink for linear motion in forward
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