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Fuzzy regression: a genetic programming approach

Fuzzy regression: a genetic programming approach,10.1109/KES.2000.885828,Thomas Feuring,Wolfgang Golubski,Mike Gassmann

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Fuzzy regression: a genetic programming approach   (Citations: 2)
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Given some data pairs (X¯i, Y¯i), 1⩽i⩽k, of fuzzy numbers, we are interested in finding a fuzzy function F which best fits the given data. Because of fuzzy arithmetic, we cannot compute a fuzzy function with F(X¯i)=Y¯ i for all i, as in the crisp case. Therefore, we used a genetic programming approach to find a suitable fuzzy function. We present some tests and argue that this method is quite suitable for obtaining a fuzzy function which can explain the given data
Conference: Knowledge-Based Intelligent Information & Engineering Systems - KES , pp. 349-352, 2000
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    • ...The ultimate objective of forming a regression model is to estimate the value of a continuous dependent variable for any arbitrary value of the independent variable [4]...
    • ...As a result, this makes the estimated model not fully efficient since by considering data instead of uncertain data, some important information may have been overlooked or neglected [4]...
    • ...As an example, we can refer to genetic algorithms [13], [38], genetic programming [4], [39], tabu search [40], and fuzzy neural networks [41]–[43]...

    M. Hadi Mashinchi, et al. A Tabu–Harmony Search-Based Approach to Fuzzy Linear Regression

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