Determining fuzzy rules for student’s performance and learning efficiency by using a hybrid approach
This paper describes a hybrid approach that combines a fuzzy inference system with a neural network, and with a rough set technique in determining the fuzzy rules from a fuzzy rule base system of the student model. The back-propagation neural-fuzzy approach is used to solve the problem of incomplete...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Inderscience Enterprises
2010
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/37272/2/inarticle.php_artid%3D34910 http://eprints.utm.my/id/eprint/37272/ |
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| Institution: | Universiti Teknologi Malaysia |
| Language: | English |
| Summary: | This paper describes a hybrid approach that combines a fuzzy inference system with a neural network, and with a rough set technique in determining the fuzzy rules from a fuzzy rule base system of the student model. The back-propagation neural-fuzzy approach is used to solve the problem of incompleteness in the decision made by the human experts. By training the neural network with selected patterns that are certain, the proposed approach was expected to produce decisions that could not previously be determined, and accordingly, a complete fuzzy rule base is formed. This paper proposes a rough-fuzzy approach that reduces the complete fuzzy rule base into a concise fuzzy base. After comparing the defuzzified values of the complete fuzzy rule base with the concise fuzzy rule base, it is discovered that the performance of the concise fuzzy rule base does not degrade and it remains complete and consistent. |
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