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Warianty tytułu
Języki publikacji
Abstrakty
This chapter describes the application of two different global optimization algorithms (CRS3 and the evolutionary strategy) to the problem of recurrent neural network learning. The performance has been compared with the standard multilayer perceptron with classical gradient learning strategy. The performance has been tested on different dynamic test models. The efficiency of the global optimization approach is shown.
Rocznik
Tom
Strony
163--172
Opis fizyczny
Bibliogr. 8 poz., tab., rys., wykr.
Twórcy
autor
- Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska 15/19, 06-665 Warsaw, Poland
autor
- Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska 15/19, 06-665 Warsaw, Poland
Bibliografia
- 1. Osowski S.: Nural networks in algorithmic approach, WNT, Warszawa 1996, (in Polish).
- 2. Osowski S.: Nural networks in information processing, PW Press, Warszawa 2000, (in Polish).
- 3. Findeisen W., Szymanowski J., Wierzbicki A.: Theory and computing methods of optimization, PWN, Warszawa, 1980, (in Polish).
- 4. Niewiadomska-Szynkieiwcz E.: the survey of global optimization algorithms, (in Polish).
- 5. Pearlmutter B.A.: Dynamic recurrent neural networks.
- 6. Proce W.L.: Global optimization algorithms for a CAD workstation, Journal of Optimization Theory and Applications, vol. 55, no 1, 1987.
- 7. Santini S., Del Bimbo A., Jain R.: Block-structured recurrent neural networks.
- 8. Box G.E.P., Jenkins G.M.: Time series analysis. Prediction and control, PWN, 1983 (in Polish).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-15acc93d-f4b5-4a2c-8be3-1c7d399c78b5