Proceedings of the 2013 International Conference on Information, Business and Education Technology (ICIBET 2013)

Applications of GRNN Based on Particle swarm algorithm Forecasting Stock Prices

Authors
Jinna Lu, Yanping Bai
Corresponding Author
Jinna Lu
Available Online March 2013.
DOI
https://doi.org/10.2991/icibet.2013.15How to use a DOI?
Abstract

Generalized regression neural network (GRNN) has very good effect on making nonlinear forecasting model with large number of stock data. Particle swarm optimization (PSO) has simple operation analysis and is easy to implement. We use PSO algorithm to optimize the GRNN in order for optimal smoothing factor and connection weights. The prediction errors of the two models are both small. The MSE error by GRNN model reaches 0.0486, while the error by PSO-GRNN model is 0.0104. The analysis shows that PSO-GRNN model is more accurate, more stabilized and more generic than GRNN model.

Copyright
© 2013, 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 2013 International Conference on Information, Business and Education Technology (ICIBET 2013)
Series
Advances in Intelligent Systems Research
Publication Date
March 2013
ISBN
10.2991/icibet.2013.15
ISSN
1951-6851
DOI
https://doi.org/10.2991/icibet.2013.15How to use a DOI?
Copyright
© 2013, 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  - Jinna Lu
AU  - Yanping Bai
PY  - 2013/03
DA  - 2013/03
TI  - Applications of GRNN Based on Particle swarm algorithm Forecasting Stock Prices
BT  - Proceedings of the 2013 International Conference on Information, Business and Education Technology (ICIBET 2013)
PB  - Atlantis Press
SP  - 69
EP  - 72
SN  - 1951-6851
UR  - https://doi.org/10.2991/icibet.2013.15
DO  - https://doi.org/10.2991/icibet.2013.15
ID  - Lu2013/03
ER  -