Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications

Method of Flatness Pattern Recognition Based on Chaos Particle Swarm Algorithm Optimization Elman Network

Authors
Zhimin Bi, Yan Wang
Corresponding Author
Zhimin Bi
Available Online November 2016.
DOI
https://doi.org/10.2991/aiea-16.2016.14How to use a DOI?
Keywords
Flatness pattern recognition; Elman neural network; Chaos particle swarm algorithm.
Abstract

In order to obtain flatness pattern recognition method of high accuracy and simple operation .this paper presents a chaotic particle swarm initialization sequence, as well as put the chaotic sequence join into the thin iterative search process after meeting some conditions, Elman neural network shape pattern recognition method to improve the convergence speed and accuracy. In neural network modeling process, using chaotic particle swarm optimization algorithm global search for the best advantage of the ability to obtain the optimal network parameters optimized to improve accuracy of the flatness pattern recognition.

Copyright
© 2016, 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 2016 International Conference on Artificial Intelligence and Engineering Applications
Series
Advances in Computer Science Research
Publication Date
November 2016
ISBN
978-94-6252-270-1
ISSN
2352-538X
DOI
https://doi.org/10.2991/aiea-16.2016.14How to use a DOI?
Copyright
© 2016, 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  - Zhimin Bi
AU  - Yan Wang
PY  - 2016/11
DA  - 2016/11
TI  - Method of Flatness Pattern Recognition Based on Chaos Particle Swarm Algorithm Optimization Elman Network
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
PB  - Atlantis Press
SP  - 74
EP  - 79
SN  - 2352-538X
UR  - https://doi.org/10.2991/aiea-16.2016.14
DO  - https://doi.org/10.2991/aiea-16.2016.14
ID  - Bi2016/11
ER  -