Paper Info
Title
A New Genetic Algorithm For Nonlinear Multiregressions Based On Generalized Choquet Integrals
Abstract
This paper gives a new genetic algorithm for nonlinear multiregression based on generalized Choquet integrals with respect to signed fuzzy measures. Unlike the previous work where the values of the signed fuzzy measure are determined by random search in a genetic algorithm with other regression coefficients together, in this. new algorithm, they are determined algebraically and, therefore, its complexity is much lower than before.
Year
DOI
Venue
2003
10.1109/FUZZ.2003.1206535
PROCEEDINGS OF THE 12TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1 AND 2
Keywords
Field
DocType
fuzzy sets,regression analysis,genetic algorithms,predictive models,regression coefficients,genetic algorithm,fuzzy systems,mathematics,computer science,integral equations,fuzzy set theory,data mining
Random search,Applied mathematics,Discrete mathematics,Nonlinear system,Control theory,Fuzzy measure theory,Fuzzy logic,Fuzzy set,Fuzzy control system,Genetic algorithm,Mathematics,Linear regression
Conference
Citations 
PageRank 
References 
18
1.79
2
Authors
2
Name
Order
Citations
PageRank
Zhenyuan Wang168490.22
Hai-Feng Guo220426.41