Application Philosophy of Fuzzy Regression
T. D. Dongale1, S. R. Ghatage2, R. R. Mudholkar3
1Mr. T. D. Dongale, School of Nanoscience and Technology, Shivaji University, Kolhapur, M.S-India.
2Mr. S. R. Ghatage, Department of Electronics, G. K. G, College, Kolhapur, M.S-India.
3Dr. R. R. Mudholkar, Department of Electronics, Shivaji University, Kolhapur, M.S-India.
Manuscript received on January 01, 2013. | Revised Manuscript received on January 02, 2013. | Manuscript published on January 05, 2013. | PP: 170-172 | Volume-2, Issue-6, January 2013. | Retrieval Number: F1140112612/2013©BEIESP
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© The Authors. Published By: 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 uncertainties and its prediction normally tend to be complex phenomena. The randomness and fuzziness are two kinds of uncertainties possible in real time. The randomness deals with the general uncertainties whereas; the fuzzy logic addresses the linguistic uncertainties. The fuzzy logic and its allied field deal with the every part of uncertainties in fuzzy way. For a situation where, complex predictions are to tackle then statistical regression methodology is used from many years. The next step in this scenario for dealing with uncertainties is the ‘Fuzzy Regression’. This paper presents the elementary theory of fuzzy regression and the philosophy behind its potential application.
Keywords: Fuzzy Logic, Fuzzy Regression, Uncertainties, Computational Intelligence