Title :
Adaptive genetic algorithm for parameter identification of centrifugal compressor
Author :
Wang Xiaogang ; Bai Xueliang ; Jiang Bo
Author_Institution :
Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China
Abstract :
Parameters of mechanism model of centrifugal compressor is wide-ranging and artificial selection is difficult to solve. Transforming parameter identification problem of the multistage compressor model into an optimization problem, Adaptive genetic algorithm (AGA) is used to decide the unknown parameters in the model. Model verification results show that the parameters identification can reflect the operating characteristics of centrifugal compressors and the precision of the model is improved.
Keywords :
compressors; genetic algorithms; parameter estimation; AGA; adaptive genetic algorithm; artificial selection; centrifugal compressor mechanism model parameter; model verification; multistage compressor model; operating characteristics; optimization problem; parameter identification problem; Adaptation models; Analytical models; Blades; Genetic algorithms; Optimization; Parameter estimation; Temperature measurement; Adaptive Genetic Algorithm; Centrifugal compressor; Mechanism model; Parameter identification;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561456
