Topology optimization of fuzzy systems for response integration in ensemble neural networks: The case of fingerprint recognition

被引：0

作者：

Lopez, M. ^{[1
]}

Melin, P. ^{[2
]}

机构：

[1] Univ Autonoma Baja California, Tijuana, Baja California, Mexico

[2] Tijuana Inst Technol, Div Grad Studies & Res, Tijuana, Baja California, Mexico

来源：

2008 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY, VOLS 1 AND 2 | 2008年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We describe in this paper a new method for response integration in ensemble neural networks with Type-1 and Type-2 Fuzzy Logic using Genetic Algorithms (GAs) for optimization. In this paper we consider pattern recognition with ensemble neural networks for the case of fingerprints. An ensemble neural network of three modules is used. Each module is a local expert on person recognition based on its biometric measure (Pattern recognition for fingerprints). The Response Integration method of the ensemble neural networks has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. Using GAs to optimize the fuzzy rules of The Type-1 and Type-2 Fuzzy System we can improve the results of the response integration. We show in this paper a comparative study of the results of a type-2 approach for response integration that improves performance over the type-1 fuzzy logic approaches.

引用

页码：738 / 743

页数：6

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