DocumentCode :
2501595
Title :
Application of PSO and QPSO algorithm to estimate parameters from kinetic model of glutamic acid batch fermetation
Author :
Lu, Kezhong ; Wang, Ruchuan
Author_Institution :
Dept. of Comput. Sci., Chizhou Coll., Chizhou
fYear :
2008
fDate :
25-27 June 2008
Firstpage :
8968
Lastpage :
8971
Abstract :
Estimation of parameters from kinetic model of batch fermentation is a tough searching problem. Unfortunately, the traditional approaches easily get stuck in a local minimum. So particle swarm optimization (QPSO) algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm were used to estimate parameters from kinetic model of batch fermentation in this paper. The result compared with artificial neural networks (ANN) and genetic algorithm (GA) shows that the estimation precision of PSO is higher than ANNpsilas and GApsilas, the estimation precision of QPSO is highest. QPSO algorithm is an effective way to estimate such kind of parameters with complex nonlinear model from kinetic model of batch fermentation.
Keywords :
batch processing (industrial); fermentation; genetic algorithms; neural nets; parameter estimation; particle swarm optimisation; search problems; QPSO algorithm; artificial neural networks; complex nonlinear model; genetic algorithm; glutamic acid batch fermentation; kinetic model; parameter estimation; quantum-behaved particle swarm optimization; searching problem; Amino acids; Application software; Artificial neural networks; Automation; Educational institutions; Intelligent control; Kinetic theory; Logistics; Parameter estimation; Particle swarm optimization; kinetics of fermentation; parameter estimation; particle swarm optimization; quantum-behaved particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
Type :
conf
DOI :
10.1109/WCICA.2008.4594347
Filename :
4594347
Link To Document :
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