Unified Entropy in Self-organizing Feature Maps Neural Network

DOI

10.2991/isaeece-17.2017.4How to use a DOI?

Keywords

Unified Entropy, Mutual Information, Self-organizing Feature Maps.

Abstract

Pattern Recognition is a very urgent research area in intelligent information processing and computer intelligent perception, such as computer vision, content-based retrieval, image-processing, etc. In general, the research on pattern recognition is carried out partial separately as feature extraction, classification, etc. in which samples of feature extraction could not be reliable and the global optimum could not been achieved. In this paper the unified entropy theory on Pattern Recognition is presented firstly, in which the information procedures in learning and recognition and the determine role of Mutual Information have been discovered. Secondly build SOFM neural network and apply Mutual Information entropy to compute reliability of training samples, through which selecting excellent data samples is presented to get optimum recognition performance, which is crucial for difficult pattern recognition problems. Experiments on device state recognition prove their effective and efficient.

Copyright

© 2017, 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/).

Download article (PDF)

TY  - CONF
AU  - Chunyang Zhu
PY  - 2017/03
DA  - 2017/03
TI  - Unified Entropy in Self-organizing Feature Maps Neural Network
BT  - Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017)
PB  - Atlantis Press
SP  - 14
EP  - 22
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-17.2017.4
DO  - 10.2991/isaeece-17.2017.4
ID  - Zhu2017/03
ER  -