The thermal process identification with radial basis function network based on quantum particle swarm optimization

Author

Wang, Dongfeng ; Wang, Zijie ; Huang, Yu ; Han, Pu

fYear

2009

fDate

6-7 April 2009

Firstpage

1

Lastpage

4

Abstract

The particle swarm optimization algorithm is an extremely effective method in evolutionary computation. But it also has some disadvantages such as finite sampling space and being easy to run into prematurity. In this paper, a new particle swarm optimization algorithm based on quantum individual is proposed (QPSO,). On basis of QPSO, a novel method of nonlinear system identification is proposed with constructing radial basis function neural network. The simulation results of a nonlinear system reveal the effectiveness of this method. A special program is compiled to identify the object model of the thermal process, and the dynamic process between primary air feed rate and bed temperature is identified. The results show that the approach is easy to be used for identification and has a certain practical value.

Keywords

boilers; identification; nonlinear systems; particle swarm optimisation; power engineering computing; quantum computing; radial basis function networks; air feed boiler rate process; bed boiler temperature process; neural network; nonlinear system identification; quantum particle swarm optimization; radial basis function network; thermal process identification; Evolutionary computation; Feeds; Neural networks; Nonlinear dynamical systems; Nonlinear systems; Particle swarm optimization; Particle tracking; Quantum computing; Radial basis function networks; Temperature; Quantum Particle Swarm Optimization; RBF; System identification; Thermal process;

fLanguage

English

Publisher

ieee

Conference_Titel

Sustainable Power Generation and Supply, 2009. SUPERGEN '09. International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4244-4934-7

Type

conf

DOI

10.1109/SUPERGEN.2009.5347896

Filename

5347896