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The granular computing in uncertain optimization problems

Identyfikatory
Warianty tytułu
Konferencja
Evolutionary Computation and Global Optimization 2008 / National Conference (11 ; 2-4.06.2008 ; Szymbark, Poland)
Języki publikacji
EN
Abstrakty
EN
The paper is devoted to applications of evolutionary algorithms in identification of structures being under the uncertain conditions. Uncertainties can occur in boundary conditions, in material or geometrical parameters of structures and are modelled by three kinds of granularity: interval mathematics, fuzzy sets and theory of probability. In order to formulate the optimization problem for such a class of problems by means of evolutionary algorithms the chromosomes are considered as interval, fuzzy and random vectors whose genes are represented by: (i) interval numbers, (ii) fuzzy numbers and (iii) random variables, respectively. Description of evolutionary algorithms with granular representation of data is presented in this paper. Various concepts of evolutionary operator such as a crossover and a mutation and methods of selections are described. In order to evaluate the fitness functions the interval, fuzzy and stochastic finite element methods are applied. Several numerical tests and examples of identification of uncertain parameters are presented.
Rocznik
Tom
Strony
173--180
Opis fizyczny
Bibliogr. 21 poz., tab., rys.
Twórcy
autor
  • Silesian University of Technology, Department for Strength of Materials and Computational Mechanics, Konarskiego 18A, 44-100 Gliwice, Poland, piotr.orantek@polsl.pl
Bibliografia
  • [1] Arabas, J., Wykłady z algorytmów ewolucyjnych. WNT, 2001.
  • [2] Arslan, A., Kaya, M., Determination of fuzzy logic membership functions using genetic algorithms. Fuzzy Sets and Systems 118 (2001) Elsevier 2001.
  • [3] Bargiela, A., Pedrycz, W., Granular Computing: An introduction. Kluwer Academic Publishers Boston/Dordrecht/London 2002.
  • [4] Bui, H.D., Inverse Problems in the Mechanics of Materials: An Introduction. CRC Pres, Bocca Raton 1994.
  • [5] Burczyński, T., Orantek, P., Application of neural networks in controlling of evolutionary algorithms. First Asian-Pacific Congress on Computational Mechanics APCOM 01. Sydney, Australia 2001.
  • [6] Burczyński, T., Skrzypczyk, J., Fuzzy aspects of the boundary element method. Engineering Analysis with Boundary Elements 19, 1997, pp. 209-216.
  • [7] Chen, L., Rao, S.S., Fuzzy finite element approach for vibrating analysis of imprecisely defined systems. Finite Elements in Analysis and Design. 1977, vol. 27, pp. 69-83.
  • [8] Cordon, O., Gomide, F., Herrera, F., Homann, F., Magdalena, I., Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems 141 (2004).
  • [9] Czogała, E., Pedrycz, W., Elementy i metody teorii zbiorów rozmytych. PWN, Warszawa 1985.
  • [10] Kacprzyk, J., Zbiory rozmyte w analizie systemowej. PWN, Warszawa 1986.
  • [11] Karr, C.L., Design of an adaptive fuzzy logic controller using a genetic algorithm, Proc. 4th Int. Conf. on Genetic Algorithms, San Diego, July 13-16, pp. 450-457.
  • [12] Kleiber, M.(ed), 1998, Handbook of Computational Solid Mechanics, Springer-Verlag, Berlin 1998.
  • [13] Orantek, P., The optimization and Identification problems ofstructures with fuzzy parameters. Proc. of 3rd European conference on Computational mechanics ECCM-2006, Lizbona, Portugal 2006.
  • [14] Papoulis, A., Probability, Random Variables, and Stochastic Processes. McGraw Hill, New York 1991.
  • [15] Pedrycz, W., Fuzzy evolutionary computing. Soft Computing 2 (1998), Springer-Verlag 1998.
  • [16] Piegat, A., Modelowanie i sterowanie rozmyte. Akademicka Oficyna Wydawnicza EXIT. Warszawa 2003.
  • [17] Rutkowska, D., Piliński, M., Rutkowski, L., Sieci neuronowe, algorytmy genetyczne i systemy rozmyte. PWN, Warszawa-Łódź 1997.
  • [18] Schaefer, R., Podstawy genetycznej optymalizacji globalnej. Wydawnictwo Uniwersytetu Jagiellońskiego, Kraków, 2002.
  • [19] Skalna, I., Zastosowanie metod algebry przedziałowej w jakościowej analizie układów mechanicznych. Praca doktorska, Politechnika Śląska, Gliwice 2002.
  • [20] Sobczyk, K., Stochastic wave propagation. PWN, Warszawa 1984.
  • [21] Zadeh, L.A., Fuzzy sets, Information and Control, Vol. 8, 1965.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA9-0035-0019
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