Pattern Recognition Algorithm Based on Closeness Degree of Triangle Fuzzy Number

DOI

10.2991/mmsa-18.2018.38How to use a DOI?

Keywords

EW-type closeness degree; triangular fuzzy numbers; relative approach degree; pattern recognition

Abstract

In this paper, for determining recognition target class problem in inaccurate or imprecise environments, we designed triangular fuzzy number pattern recognition algorithm based on closeness degree. Firstly, the attribute values of each standard test set and the sample set are converted into triangular fuzzy numbers. Secondly, the EW-type closeness formula is used to calculate closeness degree between the attributes of each standard set and sample set, and establish the relative closeness degree matrix. Thirdly, the target identification of class model is determined based on the relative closeness degree matrix. By identifying 20 kinds of perfume test data sets with the tag to verify the validity and rationality of the proposed algorithm.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yu-e Bao
AU  - Erdun Bai
PY  - 2018/03
DA  - 2018/03
TI  - Pattern Recognition Algorithm Based on Closeness Degree of Triangle Fuzzy Number
BT  - Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)
PB  - Atlantis Press
SP  - 173
EP  - 176
SN  - 1951-6851
UR  - https://doi.org/10.2991/mmsa-18.2018.38
DO  - 10.2991/mmsa-18.2018.38
ID  - Bao2018/03
ER  -