Comparative analysis of Mamdani, Larsen and Tsukamoto methods of fuzzy inference system for students’ academic performance evaluation

Tejash U. Chaudhari 1, *, Vimal B. Patel 1, Rahul G. Thakkar 2 and Chetanpal Singh 2

1 College of Agriculture, Navsari Agricultural University, Waghai, Gujarat, India.
2 Victorian Institute of Technology, Melbourne, Australia.
 
Research Article
International Journal of Science and Research Archive, 2023, 09(01), 517–523.
Article DOI: 10.30574/ijsra.2023.9.1.0443
Publication history: 
Received on 23 April 2023; revised on 13 June 2023; accepted on 16 June 2023
 
Abstract: 
Over the last few years, the use of the fuzzy logic technique for evaluating performance in the teaching-learning process is growing rapidly. In this research work, three different fuzzy inference methods: Mamdani fuzzy inference method, Larsen fuzzy inference method and Tsukamoto fuzzy inference method have been proposed for students' academic performance appraisal for multi-input variables. To obtain a degree of satisfaction, the Triangular membership function is used. The results of experiments showed the best fuzzy inference method among Mamdani, Larsen and Tsukamoto. We have also compared the results with the existing statistical method.
 
Keywords: 
Students’ academic performance evaluation; Fuzzy logic techniques; Mamdani inference method; Larsen inference method; Tsukamoto inference method; Membership Functions; Learning evaluation
 
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