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Issues in nonlinear model structure identification using genetic programming

Gray, G.J., Weinbrenner, T., Murray-Smith, D.J., Li, Y., and Sharman, K.C. (1997) Issues in nonlinear model structure identification using genetic programming. In: Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA 97), 2-4 Sep 1997, Glasgow, UK.

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Publisher's URL: http://dx.doi.org/10.1049/cp:19971198

Abstract

Genetic Programming (GP) is a powerful nonlinear optimisation tool which can be applied to the identification of the nonlinear structure of dynamic systems. Several issues must be considered. The model format must be defined and a simulation routine integrated with the GP optimisation code to evaluate each candidate model. Numerical parameters of the model must be identified and the model's "goodness-of-fit" must be quantified. The GP algorithm must be configured for model identification and optimised for computation time. Finally, general nonlinear modelling issues such as experimental design and model validation must be considered. All these issues are addressed in this paper.

Item Type:Conference Proceedings
Additional Information:IEEE Conference Publication No. 446
Keywords:Genetic Programming, optimisation, dynamic model, nonlinear, model structure, system identification, model validation, experimental design, helicopter, engine.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Prof David and Li, Prof Yun
Authors: Gray, G.J., Weinbrenner, T., Murray-Smith, D.J., Li, Y., and Sharman, K.C.
Subjects:Q Science > QA Mathematics > QA76 Computer software
T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TL Motor vehicles. Aeronautics. Astronautics
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
College of Science and Engineering > School of Engineering

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