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Automatic Selection of Input Variables and Initialization Parameters in an Adaptive Neuro Fuzzy Inference System. Application for Modeling Visual Textures in Digital Images.

Authors :
Hutchison, David
Kanade, Takeo
Kittler, Josef
Kleinberg, Jon M.
Mattern, Friedemann
Mitchell, John C.
Naor, Moni
Nierstrasz, Oscar
Pandu Rangan, C.
Steffen, Bernhard
Sudan, Madhu
Terzopoulos, Demetri
Tygar, Doug
Vardi, Moshe Y.
Weikum, Gerhard
Sandoval, Francisco
Prieto, Alberto
Cabestany, Joan
Graña, Manuel
Mejías, A.
Source :
Computational & Ambient Intelligence; 2007, p407-413, 7p
Publication Year :
2007

Abstract

In this paper we present a method for the automatic selection of input variables and some previous parameters, such as number and type of membership functions, in an Adaptive Neuro Fuzzy Inference System (ANFIS) using a Genetic Algorithm with a new fitness function. Both of them constitute a design scheme that we will use for modeling the perception of textures in Digital I-mages. Some examples are presented, training ANFIS with this scheme for mo-deling the following visual textures: coarseness, directionality and regularity. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540730064
Database :
Complementary Index
Journal :
Computational & Ambient Intelligence
Publication Type :
Book
Accession number :
33147726
Full Text :
https://doi.org/10.1007/978-3-540-73007-1_50