A Convolutional Fuzzy Neural Network Architecture for Object Classification with Small Training Database

Min Jie Hsu, Yi Hsing Chien, Wei Yen Wang, Chen Chien Hsu

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, we propose a novel architecture that combines the convolutional neural network (CNN) with a fuzzy neural network (FNN). We utilize the fuzzy neural network with semi-connected layers to sum up feature information. During the training process, to map membership values, the CNN generates feature maps as outputs and feeds into fuzzifier layers, alternatively called fuzzy maps. The proposed method increases classification accuracy, because fuzzy neural networks can generate not only crisp values but also fuzzy values; this means that there is potentially more information contained in the fuzzy set. Our model is evaluated by cross-validation tests. While big data is necessary for training in general, we train our model with small data and test with big data to demonstrate its ability of object classification in cases where sufficient data are not available.

Original languageEnglish
JournalInternational Journal of Fuzzy Systems
Volume22
Issue number1
DOIs
Publication statusPublished - 2020 Feb 1

Keywords

  • Convolutional neural network
  • Fuzzy neural network
  • Object classification
  • Small data

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'A Convolutional Fuzzy Neural Network Architecture for Object Classification with Small Training Database'. Together they form a unique fingerprint.

  • Cite this