The Data-Driven Fuzzy System with Fuzzy Subtractive Clustering for Time Series Modeling
Agus Widodo1, Samingun Handoyo2, Rudy Ariyanto3, Marji4
1Agus Widodo*, Mathematics Department, University of Brawijaya, Malang, Indonesia.
2Samingun Handoyo, Statistics Department, University of Brawijaya, Malang, Indonesia.
3Rudy Ariyanto, Informatics Engineering Department, Polythecnic of State Malang, Malang, Indonesia.
5Marji, Informatics Engineering Department, University of Brawijaya, Malang, Indonesia.
Manuscript received on December 13, 2019. | Revised Manuscript received on December 25, 2019. | Manuscript published on January 10, 2020. | PP: 3357-3362 | Volume-9 Issue-3, January 2020. | Retrieval Number: C9039019320/2020©BEIESP | DOI: 10.35940/ijitee.C9039.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The paper aims to identify input variables of fuzzy systems, generate fuzzy rule bases by using the fuzzy subtractive clustering, and apply fuzzy system of Takagi Sugeno to predict rice stocks in Indonesia. The monthly rice procurement dataset in the period January 2000 to March 2017 are divided into training data (January 2000 to March 2016 and testing data (April 2016 to March 2017). The results of identification of the fuzzy system input variables are lags as system input includingYt-1, Yt-2,.Yt-6 Yt-7, Yt-11, Yt-12, and Yt-13.The Input-output clustering fuzzy subtractive and selecting optimal groups by using the cluster thigness measures indicator produced 4 fuzzy rules. The fuzzy system performance in the training data has a value of R2 of 0.8582, while the testing data produces an R2 of 0.7513.
Keywords: Fuzzy System, Generating Rule Bases, Subtractive Clustering. System Performance
Scope of the Article: Clustering