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Tytuł artykułu

Generation and Optimization of Fuzzy Neural Networks Structure

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Generowanie i optymalizacja struktury rozmytej sieci neuronowej
Języki publikacji
EN
Abstrakty
EN
The paper presents the possibility of application of genetic algorithms for optimization of fuzzy neural networks structure and its application to pattern recognition. A genetic method to generate a fuzzy neural network, which has both structure and synapse weights adequate for a given task is proposed.
PL
W pracy zaprezentowano możliwość zastosowania algorytmów genetycznych do optymalizacji struktury rozmytej sieci neuronowej. Przedstawiono przykład wykorzystania romytej sieci neuronowej do rozpoznania wzorców Operacje genetyczne zostały użyte do dostosowania struktury sieci i wag synaptycznych. Dostosowanie ma charakter dynamiczny, wykorzystywany jest rozproszony algorytm genetyczny. Wyniki uzyskano w języku C++ w środowiski Windows.
Rocznik
Strony
125--138
Opis fizyczny
Bibliogr. 20 poz.
Twórcy
autor
  • Military University of Technology, Department of Logistic Management, Logistic Institute, Kaliskiego 2, 00-908 Warsaw, zswiat@wat.waw.pl
Bibliografia
  • [1] V. Kvasnićka, AT ALL, Introduction to Theory of Neural Networks, Iris, Bratislava, Slovak Republic, 1997.
  • [2] Z. Świątnicki, R. Wantoch-Rekowski, Neural Networks: Introduction, Dom Wydawniczy Bellona, Warszawa, Poland, 1999.
  • [3] R. P. Lippman, An Introduction to Computing with Neural Nets, IEEE ASSP Magazine, No. 4 (1987), 4-22.
  • [4] J. J. Buckley, Numerical Relationships Between Neural Networks, Continuous Function, and Fuzzy Systems, Fuzzy Sets and Systems, Vol. 60 (1993), 1-8.
  • [5] V. Olej, J. Krupka, Analysis of Decision Processes of Automation Control Systems with Uncertainty. [Scientific Monograph], Technical University Press, Elfa, Kośice, Slovak Republic, 1996.
  • [6] M. Lehotský, V. Olej, J. Chmurny, Pattern Recognition Based on the Fuzzy Neural Networks and their Learning by Modified Genetic Algorithms, Neural Network World, Vol. 5, No. 1 (1995), 91-97.
  • [7] J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor (1975).
  • [8] P. Spiessens, Genetic Algorithms, AI MEMO, No. 88-19, Vrije Universit Brussel, 1988.
  • [9] F. Hoffmeister, T. Back, Genetic Algorithms and Evolution Strategies. Similarities and Differences, Proc. of 1st Workshop on Parallel Problem Solving from Nature, Dortmund, Germany, 1990, 455-469.
  • [10] V. Olej, Realization of Distributed Genetic Algorithms and Evolution Strategies, Proc. of 17th International Conference on Artificial Intelligence and Information -Control Systems of Robots, World Scientific, Printed in Singapore by Uto - Print, 1997, 277-285.
  • [11] N. J. Radcliffe, The Algebra of Genetic Algorithms, Annals of Mathematics and Artificial Intelligence, Vol. 10 (1994), 339-384.
  • [12] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison - Wesley, Reading, MA, 1989.
  • [13] A. Cangelosi, D. Paris, S. Nolfi, Cell Division and Migration in a ‘Genotype' for Neural Networks, Network: Computation in Neural Systems, Vol. 5 (1994), 497-515.
  • [14] T. Nagao, T. Agui, H. Nagahashi, A Genetic Method for Optimization of Asynchronous Random Neural Networks and its Application to Action Control, Proc. of International Joint Conference on Neural Networks '93, Nagoya, Japan 1993, 1-4.
  • [15] D. Whitley, T. Starkweather, C. Bogart, Genetic Algorithms and Neural Networks: Optimizing Connection and Connectivity. Parallel Computing, North Holland, Vol. 14 (1990), 347-361.
  • [16] H. Adeli, S. L. Hung, Machine Learning - Neural Networks, Genetic Algorithms, and Fuzzy Systems, John Wiley and Sons, Inc., New York 1995.
  • [17] C. Nikolopoulos, Y. R. Hwang, Evolutionary Topology Configuration of Neural Nets, Neural Network World, Vol. 5 (1994), 553-566.
  • [18] J. J. Buckley, Y. Hayashi, Hybrid Neural Networks can be Fuzzy Controllers and Fuzzy Expert Systems, Fuzzy Sets and Systems, Vol. 60 (1993), 135-142.
  • [19] J. Godjevac, Comparison between Classical and Fuzzy Neurons, Proc. of 2nd European Congress on Fuzzy Intelligent Technologies, Vol. 1, Aachen, Germany 1994, 1326-1332.
  • [20] E. Uchino, T. Yamakawa, M. Kohno, Restoration of Saturated and/or Internittent Signal by using a Neo-Fuzzy-Neuron, Proc. of 2nd European Congress on Fuzzy Intelligent Technologies, Vol. 1, Aachen, Germany 1994, 170-173.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA2-0006-0011
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