DocumentCode :
2041423
Title :
Evolutionary grey-box modelling for practical systems
Author :
Tan, Kay Chen ; Li, Yun ; Gawthrop, Peter J. ; Glidle, Andrew
Author_Institution :
Centre for Syst. & Control, Glasgow Univ., UK
fYear :
1997
fDate :
2-4 Sep 1997
Firstpage :
369
Lastpage :
375
Abstract :
A novel grey box modelling methodology combining advantages of both black and clear boxes is proposed. The technique makes the best use of a priori knowledge on the clear box global structure of a physical system, whilst it incorporates accurate black boxes for unmeasurable local nonlinearities. Through hybrid genetic evolution and Boltzmann learning, it enables dominant structural modelling with local parametric tuning, without the need for linear parametrisation. Validation results show that the proposed method offers robust, uncluttered and accurate models for two practical systems. It is expected that this type of grey box model will accommodate many practical systems
Keywords :
modelling; Boltzmann learning; a priori knowledge; accurate black boxes; accurate models; clear box global structure; dominant structural modelling; evolutionary grey box modelling; grey box modelling methodology; hybrid genetic evolution; local parametric tuning; physical system; practical systems; unmeasurable local nonlinearities;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Genetic Algorithms in Engineering Systems: Innovations and Applications, 1997. GALESIA 97. Second International Conference On (Conf. Publ. No. 446)
Conference_Location :
Glasgow
ISSN :
0537-9989
Print_ISBN :
0-85296-693-8
Type :
conf
DOI :
10.1049/cp:19971208
Filename :
681053
Link To Document :
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