Multiclass Classification Based on the Convolutional Fuzzy Neural Networks

被引：2
|
作者：
Borisov, V. V. [1 ]
Korshunova, K. P. [1 ]
机构：
[1] Branch Natl Res Univ, Moscow Power Engn Inst Smolensk, Moscow, Russia
来源：
ARTIFICIAL INTELLIGENCE: (RCAI 2019) | 2019年 / 1093卷
关键词：
Multiclass classification; Fuzzy clustering; Convolutional fuzzy neural networks; MULTILAYER PERCEPTRON;
D O I：
10.1007/978-3-030-30763-9_19
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper presents a solution to the multiclass classification problem based on the Convolutional Fuzzy Neural Networks. The proposed model includes a fuzzy self-organization layer for data clustering (in addition to convolutional, pooling and fully-connected layers). The model combines the power of convolutional neural networks and fuzzy logic, it is capable of handling uncertainty and imprecision in the input pattern representation. The Convolutional Fuzzy Neural Networks could provide better accuracy in multiclass classification tasks when classified objects are often characterized by uncertainty and inaccuracy in their representation.
引用
收藏
页码：226 / 233
页数：8
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