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Evolutionary bands for the expected response in nonlinear regression

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Warianty tytułu
Konferencja
Evolutionary Computation and Global Optimization 2006 / National Conference (9 ; 31.05-2.06.2006 ; Murzasichle, Poland)
Języki publikacji
EN
Abstrakty
EN
In this paper evolutionary algorithms are applied to computation of confidence intervals for the expected response of nonlinear models. A simple phenotypic evolutionary algorithm was adapted to deal with nonlinear constraints and utilized to find the maximum and minimum value of a nonlinear model responses inside a confidence region. Moreover, the adequacy of the proposed approach is tested in a series of numerical simulations, and compared with the commonly applied linearization technique.
Rocznik
Tom
Strony
359--364
Opis fizyczny
Bibliogr. 10 poz., tab., wykr.
Twórcy
autor
Bibliografia
  • [1] Bates, D.M. and Watts, D.G. Nonlinear Regression Analysis and Its Application. Wiley & Sons, New York, 1988.
  • [2] Chryssolouris G., Lee M. and Ramsey A.Confidence interval prediction for neural network models. - IEEE Trans. Neural Networks, Vol. 7, No. 1, pp.229-232, 1996.
  • [3] J.R. Donaldson, R.B. Schnabel .Computational Experience With Confidence Regions and Confidence Intervals for Nonlinear Least Squares. - Technometrics, Vol. 29, No. 1, pp. 67-82, 1987.
  • [4] R. Galar, Evolutionary search with soft selection. - Biological Cybernetics, Vol. 60, 1989, pp. 357-364.
  • [5] Korbicz J., Kościelny J.M., Kowalczuk Z. and Cholewa W. (Eds.) (2004): Fault Diagnosis. Models, Artificial Intelligence, Applications. - Berlin: Springer-Verlag.
  • [6] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Berlin Heidelberg, 1996.
  • [7] A. Obuchowicz, P. Prętki, Phenotypic evolution with mutation based on symmetric α-stable distribution. - International Journal of Applied Mathematics And Computer Science, 2004, vol. 14, No. 3, 289-316.
  • [8] P. Pretki, M. Witczak, Assessment and minimization of parametric uncertainty for multi-output neural networks - application to fault diagnosis. In: Recent Developments in Artificial Intelligence Methods - AI-METH 2005. Gliwice, Polska, pp. 155-160.
  • [9] R.D. De Veaux, J. Schweinsberg, J. Schumi, L.H. Ungar, Prediction Intervals for Neural Networks via Nonlinear Regression. - Technometrics, Vol. 40, No. 4, pp. 273-282, 1998.
  • [10] M. Witczak, Advances in model-based fault diagnosis with evolutionary algorithms and neural networks. - International Journal of Applied Mathematics And Computer Science, 2006, vol. 16, No. 1, 85-99.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA9-0052-0038
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