Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering

Coal Mine Robot Binocular Vision Recognition System Based on Fuzzy Neural Network

Authors
C.C. Shang, H.W. Ma
Corresponding Author
C.C. Shang
Available Online July 2015.
DOI
10.2991/eame-15.2015.26How to use a DOI?
Keywords
fuzzy neural network; binocular vision; coal mine detection robot
Abstract

In general, coal mine detection robot has characteristics of a poor adaption to uncertain underground environment in the process of rescue. This paper proposed a binocular vision recognition system based on fuzzy neural network. The system in view of general fuzzy neural network, adopt self–organizing learning algorithms, and add fuzzy rules and membership function parameters to obtain an improved fuzzy neural network algorithm, which will reduce errors during the recognition process of coal mine detection robot. The simulation results and actual underground measurements show that the system has a higher accuracy and shorter respond time.

Copyright
© 2015, 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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Volume Title
Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering
Series
Advances in Engineering Research
Publication Date
July 2015
ISBN
978-94-62520-71-4
ISSN
2352-5401
DOI
10.2991/eame-15.2015.26How to use a DOI?
Copyright
© 2015, 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/).

Cite this article

TY  - CONF
AU  - C.C. Shang
AU  - H.W. Ma
PY  - 2015/07
DA  - 2015/07
TI  - Coal Mine Robot Binocular Vision Recognition System Based on Fuzzy Neural Network
BT  - Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering
PB  - Atlantis Press
SP  - 95
EP  - 98
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-15.2015.26
DO  - 10.2991/eame-15.2015.26
ID  - Shang2015/07
ER  -