Research on the Control System of Greenhouse Based on Particle Swarm and Neural Network

DOI

10.2991/itoec-15.2015.35How to use a DOI?

Keywords

Greenhouse controlling, neural network, particle swarm, system design

Abstract

In terms of problems from the quantification factor and scaling factor for fuzzy controller in networked control systems (NCS), which are hard to tackle with conventional empirical methods, the improved quantum particle swarm optimization (IQPSO) based on adaptive mutation of the artificial bee colony operator is proposed in this paper, which is inspired by the thought of searching for nectar source in artificial bee colony algorithm (ABC algorithm) and the performance test is conducted against three types of typical test functions. Then IQPSO is applied into the parameter optimization of fuzzy controller in NCS with time delays, and one typical case in the industrial process control is used to perform the simulated experiment, of which the results indicate that fuzzy controller designed with the aid of IQPSO algorithm PID controller is of better control effect and higher adaptive capacity than those of the PID controller designed with IQPSO and the fuzzy controller designed with standard QPSO algorithm.

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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TY  - CONF
AU  - Jun Wang
AU  - Haiye Yu
PY  - 2015/03
DA  - 2015/03
TI  - Research on the Control System of Greenhouse Based on Particle Swarm and Neural Network
BT  - Proceedings of the 2015 Information Technology and Mechatronics Engineering Conference
PB  - Atlantis Press
SP  - 164
EP  - 168
SN  - 2352-538X
UR  - https://doi.org/10.2991/itoec-15.2015.35
DO  - 10.2991/itoec-15.2015.35
ID  - Wang2015/03
ER  -