Title :
Fuzzy regression: a genetic programming approach
Author :
Feuring, Thomas ; Golubski, Wolfgang ; Gassmann, Mike
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Siegen Univ., Germany
Abstract :
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
Keywords :
arithmetic; function approximation; fuzzy set theory; genetic algorithms; mathematics computing; statistical analysis; fuzzy arithmetic; fuzzy function; fuzzy number data pairs; fuzzy regression; genetic programming; Arithmetic; Fuzzy sets; Fuzzy systems; Genetic algorithms; Genetic programming; Intelligent systems; Paper technology; Regression analysis; Uncertainty;
Conference_Titel :
Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-6400-7
DOI :
10.1109/KES.2000.885828
