A Learn of Fuzzy Regression Model and Its Applications
B. Anandhavel1, T. Edwin Prabakaran2
1B. Anandhavel, Assistant Professor, Department of Statistics, DRBCCC Hindu College, Pattabiram, Chennai- 600072., University of Madras.
2Dr. T. Edwin Prabakaran, Associate Professor, Department of Statistics, Loyola, College, Chennai – 600034., University of Madras.
Manuscript received on 06 March 2019 | Revised Manuscript received on 15 March 2019 | Manuscript published on 30 July 2019 | PP: 2967-2971 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2415078219/19©BEIESP | DOI: 10.35940/ijrte.B2415.078219
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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: Many statistics report shown in fuzzy module into clear problems using the centroid system, consequently we will research the usual linear regression model which is modified from the fuzzy linear regression model. The models enter and generate fuzzy numbers, and the regression coefficients are clear numbers. Hybrid algorithms are considered to fit the fuzzy regression model. So that the validity and quality of the suggested methods can be guaranteed. Therefore,the parameter estimation and have an impact on evaluation situated on knowledge deletion. By way of the gain knowledge of example and evaluation with other model, it may be concluded that the model in this paper is utilized without difficulty and better.
Keyword: Fuzzy Linear Regression Model, Centroid Method, Data Deletion Model, Parameter Estimation.
Scope of the Article: Fuzzy Logics