• 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