Hierarchical Genetic Algorithm for Type-2 fuzzy Integration applied to Human Recognition

Author

Sanchez, Dominick ; Melin, Patricia

Author_Institution

Tijuana Inst. Technol., Tijuana, Mexico

fYear

2013

fDate

24-28 June 2013

Firstpage

298

Lastpage

303

Abstract

In this paper a new model of a Hierarchical Genetic Algorithm (HGA) for fuzzy inference system optimization is proposed. The proposed HGA optimizes the fuzzy integrators architecture (type of system, number of trapezoidal membership functions, and their parameters). The model was applied to pattern recognition based on the iris, ear and voice biometrics. Fuzzy logic is used as a method for modular neural networks (MNNs) response integration.

Keywords

fuzzy logic; fuzzy reasoning; fuzzy set theory; genetic algorithms; iris recognition; neural nets; optimisation; speech recognition; HGA; MNN response integration; ear biometrics; fuzzy inference system optimization; fuzzy integrators architecture; fuzzy logic; hierarchical genetic algorithm; human recognition; iris biometrics; modular neural networks response integration; pattern recognition; type-2 fuzzy integration; voice biometrics; Ear; Genetic algorithms; Image recognition; Iris recognition; Neural networks; Training; Granular computing; Hierarchical Genetic Algorithms; Modular Neural Networks; Optimization; Type-2 Fuzzy Logic;

fLanguage

English

Publisher

ieee

Conference_Titel

IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint

Conference_Location

Edmonton, AB

Type

conf

DOI

10.1109/IFSA-NAFIPS.2013.6608416

Filename

6608416