Particle Swarm Optimization PID Neural Network Control Method in the Main Steam Temperature Control System

Author

Wei, Liu ; Junmin, Zhou

Author_Institution

Dept. of Phys. & Electrionic Eng., Zhoukou Normal Univ., Zhoukou, China

Volume

2

fYear

2012

fDate

23-25 March 2012

Firstpage

137

Lastpage

140

Abstract

BP algorithm based on the gradient descent depends on initial weight selection with slow convergence rate and easily falling into local optimum. This paper presents the PSO algorithm and BP algorithm respectively in the global and local search advantage for the neural network weights optimization, The algorithm was used for the main steam temperature control system. The control strategy improved the control performance, and had a good anti-jamming performance and strong robustness, it achieved good control effect for large delay and variable object.

Keywords

backpropagation; gradient methods; neurocontrollers; particle swarm optimisation; steam power stations; temperature control; three-term control; BP algorithm; PID neural network control method; backpropagation; convergence rate; delay object; global search advantage; gradient descent method; initial weight selection; local search advantage; main steam temperature control system; neural network weights optimization; particle swarm optimization; proportional-integral-derivative control; variable object; Biological neural networks; Delay; Particle swarm optimization; Temperature; Temperature control; Transfer functions; BP algorithm; Main steam temperature control; Neural network; PSO algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4673-0689-8

Type

conf

DOI

10.1109/ICCSEE.2012.289

Filename

6187984