Response Integration in Modular Neural Networks using Choquet Integral with Interval Type 2 Sugeno Measures

被引：0

作者：

Martinez, Gabriela E. ^{[1
]}

Mendoza, Olivia ^{[1
]}

Castro, Juan R. ^{[1
]}

Rodriguez-Diaz, A. ^{[1
]}

Melin, Patricia ^{[2
]}

Castillo, Oscar ^{[2
]}

机构：

[1] Autonomous Univ Baja California, Fac Chem Sci & Engn, Tijuana, Mexico

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

来源：

2015 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY DIGIPEN NAFIPS 2015 | 2015年

关键词：

Aggregation operators; Choquet integral; Sugeno integral; modular neural networks; fuzzy measures; fuzzy densities; RECOGNITION; OPTIMIZATION;

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：

In this paper a new method for response integration, based on the Choquet Integral with Interval type-2 Sugeno measures is presented. The Choquet integral is used as a method to integrate the outputs of the modules of the modular neural networks (MNN). The fuzzy Sugeno measures of the Choquet integral are represented by an interval type-2 fuzzy system. A database of faces was used to perform the preprocessing, the training, and the combination of information sources of the MNN. Type-1 and interval type-2 fuzzy systems for edge detection based on the Sobel and Morphological gradient are used, which is a pre-processing applied to the training data for better performance in the MNN.

引用

页数：6

共 30 条

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[8] [Anonymous], ADV RES DEV DIGIT SY
[9] [Anonymous], DIAGNOSIS BREAST CAN
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