Proceedings of the 2014 International Conference on Computer Science and Electronic Technology

An Improved Neural Network Optimization Method

Authors
Wen-long Yin, Tian-hui Zhang, Li-hua Guo, Jie Tao
Corresponding Author
Wen-long Yin
Available Online January 2015.
DOI
https://doi.org/10.2991/iccset-14.2015.6How to use a DOI?
Keywords
fiber optic gyro; temperature drift; artificial neural network; genetic algorithm
Abstract

In order to solve the problem of temperature drift for fiber optic gyroscope, the neural network is used to construct the temperature drift model. The neural network is easy to fall into local minimum, the convergence speed is slow, the genetic algorithm is used to optimize neural network. The genetic algorithm has the problem of premature convergence is early in species, the mixed algorithm is used, the genetic algorithm is improved. The simulation results show that the neural network optimization method can predict the temperature of fiber optic gyroscope drift effectively.

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 2014 International Conference on Computer Science and Electronic Technology
Series
Advances in Computer Science Research
Publication Date
January 2015
ISBN
978-94-62520-47-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/iccset-14.2015.6How 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  - Wen-long Yin
AU  - Tian-hui Zhang
AU  - Li-hua Guo
AU  - Jie Tao
PY  - 2015/01
DA  - 2015/01
TI  - An Improved Neural Network Optimization Method
BT  - Proceedings of the 2014 International Conference on Computer Science and Electronic Technology
PB  - Atlantis Press
SP  - 27
EP  - 30
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccset-14.2015.6
DO  - https://doi.org/10.2991/iccset-14.2015.6
ID  - Yin2015/01
ER  -