T-norm of yager class of subsethood defuzzification: improving enrolment forecast in fuzzy time series / Nazirah Ramli and Abu Osman Md. Tap

• Main Authors: Ramli, Nazirah (Author), Md. Tap, Abu Osman (Author)
• Format: Book
• Published: Universiti Teknologi MARA Cawangan Pahang, 2006.
• Subjects: Article; PeerReviewed
• Online Access: Link Metadata
• Notes: https://ir.uitm.edu.my/id/eprint/35954/1/35954.PDF
• Keywords: Fuzzy arithmetic; Error analysis (Mathematics). Theory of errors. Least squares; Problems, exercises, etc.

Fuzzy time series has been used to model observations that contain multiple values. This paper proposes the t-norm of Yager class of subsethood defuzzification to forecast university enrolments based on fuzzy time series and the data of historical enrolments which are adopted from Song and Chissom (1994). The proposed method applied seven and ten interval with equal length and the max-product and max-min as the composition operator in the fuzzy relations F(t)= F(t-l)oR(t,t-l). The result shows that the t-norm of Yager class of subsethood defuzzification models with (10, max-product) is the best forecasting method in terms of accuracy. The proposed method has also improved the forecasting results by previous researchers.