A new particle swarm optimization and the application in the soft sensor modeling

Author

Dang, Mingmei ; Wang, Zhenlei ; Qian, Feng

Author_Institution

East China Univ. of Sci. & Technol., Shanghai, China

fYear

2010

fDate

9-11 June 2010

Firstpage

1175

Lastpage

1177

Abstract

Aiming at the disadvantages of the standard Particle Swarm Optimization (PSO), a new particle swarm optimization algorithm based on dual mutation(DDPSO) is proposed. By comparing and analyzing the results of several Benchmark functions, the excellent performance of PSO is proved. The improved PSO is applied to optimize the structure and parameters in artificial neural network(ANN). The availability of algorithm optimizing neural network is proved by applying ANN in soft sensor modeling of propylene concentration measurement.

Keywords

chemical sensors; chemical variables measurement; computerised instrumentation; neural nets; particle swarm optimisation; ANN; DDPSO; algorithm optimizing neural network; artificial neural network; benchmark function; dual mutation; particle swarm optimization; propylene concentration measurement; soft sensor modeling; Ant colony optimization; Artificial neural networks; Automatic control; Chemical processes; Chemical technology; Clustering algorithms; Convergence; Educational technology; Particle swarm optimization; Particle tracking; dual mutation; modeling; neural network; particle swarm optimization; soft sensor;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Automation (ICCA), 2010 8th IEEE International Conference on

Conference_Location

Xiamen

ISSN

1948-3449

Print_ISBN

978-1-4244-5195-1

Electronic_ISBN

1948-3449

Type

conf

DOI

10.1109/ICCA.2010.5524322

Filename

5524322