Fuzzy evolutionary algorithms and neural networks in uncertain optimization problems

• Autorzy: Orantek P.
• Konferencja: Neural Networks and Soft Computing/International Symposium (30.06-02.07.2005 ; Cracow, Poland)
• Języki publikacji: EN

Abstrakty

EN

This paper is devoted to the application of the evolutionary algorithms and artificial neural networks to uncertain optimization problems in which some parameters are described by fuzzy numbers. The special method of global optimization: Two-Stages Fuzzy Strategy (TSFS) for structures in uncertain conditions is proposed. As the first stage of the TSFS the fuzzy evolutionary algorithm is used. As the second stage the local optimization method with neuro-computing is proposed. The presented approach is applied in the identification problems of mechanical structures, in which material parameters and loadings are uncertain. To solve the direct problem the fuzzy boundary element method (FBEM) is used. Several numerical tests and examples are presented.

Słowa kluczowe

EN

fuzzy evolutionary algorithm; neural network; optimization problem

PL

sieci neuronowe; rozmyty algorytm ewolucyjny

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Mechanics and Engineering Sciences
• Rocznik: 2007
• Tom: Vol. 14, No. 2
• Strony: 317--329
• Opis fizyczny: Bibliogr. 19 poz., rys.

Twórcy

autor

Orantek P.

• Department for Strength of Materials and Computational Mechanics Silesian University of Technology, Konarskiego 18a, 44-100 Gliwice, Poland

Bibliografia

• [1] J. Arabas. Wykłady z algorytmów ewolucyjnych. WNT, 2001.
• [2] A. Arslan, M. Kaya. Determination of fuzzy logic membership functions using genetic algorithms. Fuzzy Sets and Systems, 118: 297-306, 2001.
• [3] A. Bargiela, W. Pedrycz. Granular Computing: An introduction. Kluwer Academic Publishers, Boston-Dordrecht-London, 2002.
• [4] CM. Bishop. Neural networks for pattern recognition. Amazon, 1995.
• [5] T. Burczyński, P. Orantek. Application of neural networks in controlling of evolutionary algorithms. First Asian-Pacific Congress on Computational Mechanics, APCOM 01, Sydney, Australia, 2001.
• [6] T. Burczyński, J. Skrzypczyk. Fuzzy aspects of the boundary element method. Engineering Analysis with Boundary Elements, 19: 209-216, 1997.
• [7] L. Chen, S.S. Rao. Fuzzy finite element approach for vibrating analysis of imprecisely defined systems. Finite Elements in Analysis and Design, 27: 69-83, 1977.
• [8] O. Cordon, F. Gomide, F. Herrera, F. Homann, L. Magdalena. Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems, 141: 5-31, 2004.
• [9] E. Czogała, W. Pedrycz. Elementy i Metody Teorii Zbiorów Rozmytych. PWN, Warszawa 1985.
• [10] J. Kacprzyk. Zbiory Rozmyte w Analizie Systemowej. PWN Warszawa, 1986.
• [11] CL. Karr. Design of an adaptive fuzzy logic controller using a genetic algorithm. Proc. 4th Int. Conf. on Genetic Algorithms, San Diego, July 13-16, 1991, pp. 450-457.
• [12] M. Littman, D. Ackley. Adaptation in constant utility nonstationary environment. Proc. ICGA'91, 1994.
• [13] W. Pedrycz. Fuzzy evolutionary computing. Soft Computing, 2: 61-72, 1998.
• [14] A. Piegat. Modelowanie i Sterowanie Rozmyte. Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2003.
• [15] D. Rutkowska, M. Piliński, L. Rutkowski. Sieci Neuronowe, Algorytmy Genetyczne i Systemy Rozmyte. PWN, Warszawa-Łódź, 1997.
• [16] R. Schaefer. Podstawy Genetycznej Optymalizacji Globalnej. WUJ, Kraków, 2002.
• [17] I. Skalna. Zastosowanie Metod Algebry Przedziałowej w Jakościowej Analizie Układów Mechanicznych (praca doktorska). Politechnika Śląska, Gliwice, 2002.
• [18] J. Skrzypczyk, T. Burczyński. Theoretical and computational aspects of the fuzzy boundary element methods. In: T. Burczyński, ed., Advanced Mathematical and Computational Mechanics Aspects of Boundary Element Method, pp. 351-364. Kluwer, 2001.
• [19] L.A. Zadeh. Fuzzy sets. Information and Control, 8: 338-353, 1965.