Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)

The study of PSO-RBF neural network generalized predictive control strategy in unit plant

Authors
Hui Wang, Hujun Ling, Lei Pan
Corresponding Author
Hui Wang
Available Online September 2016.
DOI
https://doi.org/10.2991/amitp-16.2016.14How to use a DOI?
Keywords
particle swarm optimization algorithm RBF neural network generalized predictive control generating unit
Abstract
Unit coordinated control in thermal power plants is a system which is complex,nonlinear and is difficulty to establish accurate model, So it is hard to make system gain optimum running effect with conventional control strategy. PSO-RBF neural network is used to identify the mathematical model of coordinated control system and acts as a predictive model in generalized predictive control strategy, which is to achieves predictive control with online rolling optimization and real time feedback revision. Simulation results show that it has a strong robustness when the load condition changes,or big lag affects.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)
Part of series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-245-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/amitp-16.2016.14How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Hui Wang
AU  - Hujun Ling
AU  - Lei Pan
PY  - 2016/09
DA  - 2016/09
TI  - The study of PSO-RBF neural network generalized predictive control strategy in unit plant
BT  - 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)
PB  - Atlantis Press
SP  - 72
EP  - 76
SN  - 2352-538X
UR  - https://doi.org/10.2991/amitp-16.2016.14
DO  - https://doi.org/10.2991/amitp-16.2016.14
ID  - Wang2016/09
ER  -