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Linear and non-linear fuzzy regression: Evolutionary algorithm solutions

✍ Scribed by James J. Buckley; Thomas Feuring

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
149 KB
Volume
112
Category
Article
ISSN
0165-0114

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✦ Synopsis

Given some data, which consists of pairs of fuzzy numbers, our evolutionary algorithm searches our library of fuzzy functions (which includes linear, polynomial, exponential and logarithmic) for a fuzzy function which best ΓΏts the data. Tests of our fuzzy regression package are given for each of the four cases: linear, polynomial, exponential and logarithmic. For the linear model we also consider multiple independent variables. In all cases we use data generated with and without "noise". We prove that fuzzy polynomial regression can model the extension principle extension of continuous real-valued functions.

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