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Present work considers the neural elements (NE) with generalized threshold activation function and represents iterative method of their synthesis. Algorithm of vectors finding for structures of the neural elements with generalized threshold activation function was developed, and the sufficient condition of Boolean function unreliazability on such neural elements was discovered.
Czasopismo
Rocznik
Tom
Strony
37--42
Opis fizyczny
Bibliogr. 29 poz., wz.
Twórcy
autor
- Uzhhorod National University
autor
- Lviv Polytechnic National University
autor
- Uzhhorod National University
autor
- Lviv Polytechnic National University
Bibliografia
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- 5. Zaychenko Y. P., Kelestin Y. V. and Sevaee Fatma. 2006. Comparative effectiveness analysis of indistinct neural networks in forecasting tasks in sphere of economics and finance. Kyiv: System investigations and information technologies. Nr 1, 100-110. (in Russian).
- 6. Geche F., Kotsovsky V., Batyuk A., Geche S. and Vashkeba M. 2015. Synthesis of Time Series Forecasting Scheme Based on Forecasting Models System. Lviv: Proceedings of the 11th International Conference «ICTERI 2015», 121-136.
- 7. Geche S. F. 2012. Forecasting of effectiveness evaluation indicators in usage of main assets of enterprise within neural basis. Uzhhorod: Scientific herald of Uzhhorod University. Series: Economics. Issue 3(37), 63-67. (in Ukrainian).
- 8. Bodyanskiy Ye. and Vynokurova O. 2011. Hybrid type-2 wavelet-neuro-fuzzy network for prediction of business processes. Wroclaw: Business Informatics. Nr 21, 9-21.
- 9. Rybytska O. and Vovk M. 2014. An application of the fuzzy set theory and fuzzy logic to the problem of predicting the value of goods rests. Lublin-Rzeszow: ECONTECHMOD. Vol.3, Nr 2, 65-69.
- 10. Gunasundari S. and Baskar S. 2009. Application of Artificial Neural Network in identification of lung diseases. Nature & Biologically Inspired Computing (IEEE). 1441–1444.
- 11. Bodyanskiy Ye., Kolchygin B. and Pliss I. 2011. Adaptive neuro-fuzzy Kohonen network with variable fuzzifier. Sofia: Int. J. Information Theories & Applications. Vol. 18, Nr 3, 215-223.
- 12. Komashinskiy V. I. and Smirnov D.A. 2002. Neural networks and their application in system of control and communication. Moscow: Hot line –Telecom. 96. (in Russian).
- 13. Lytvyn V., Semotuyk O. and Moroz O. 2013. Definition of the semantic metrics on the basis of thesaurus of subjects area. Lublin-Rzeszow: ECONTECHMOD. Vol. 2, Nr 4, 47-51.
- 14. Rozenblatt F. 1965. Principles of neural dynamics. Perceptrons and the theory of brain mechanisms. Moscow: World. 480. (in Russian).
- 15. Ayzenberh N. N. and Ivaskiv Y. L. 1977. Multilevel threshold logic. Kiev: Scientific mind. 145. (in Russian).
- 16. Aizenberg I. 2010. A Periodic Activation Function and a Modified Learning Algorithm for the Multi-Valued Neuron. IEEE Transaction on Neural Networks (IEEE). Vol. 21, Nr 12, 1939-1949.
- 17. Aizenberg I. 2011. Complex-Valued Neural Networks with Multi-Valued Neurons. Berlin-Heidelberg: Springer. 264.
- 18. Bodyanskiy Y. V., Kucherenko V. Y., Kucherenko Y. I., Mykhalyov O. I. and Filatov V. A. 2008. Hybrid neuro- phase models and multiagent technologies in complicated systems. Dnipropetrovsk: System technologies. 403. (in Ukrainian).
- 19. Tkachenko R. O. and Tsmots I. H. 1999. Accelerator for realization of artificial neural networks on the basis of neuroparadigm «Functionality on set of table functions». Kiev: Collection of scientific works of Institute of modeling problems in power engineering named after H.Y. Pukhova, National Academy of Sciences of Ukraine. Issue 7, 20-28. (in Ukrainian).
- 20. Khaykin S. 2006. Neural networks: complete course. Moscow: Williams-Telekom. 1104. (in Russian).
- 21. Geche F., Batyuk A., Kotsovsky V. and Gromaszek K. 2013. Synthesis of generalized neural elements using approximation method. Warszawa: Elektronika. Nr 8, 67-69.
- 22. Curtis Ch. and Rainer I., 1969. Theory of representation of final groups and associative algebras. Moscow: Science. 667. (in Russian).
- 23. Van der Waerden B. L. 1979. Algebra. Moscow: Science.623. (in Russian).
- 24. Holubov B. I., Efimov A. V. and Skvortsov V. A. 1987. Walsh series and transforms. Theory and application. Moscow: Science. 343. (in Russian).
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- 29. Geche F. E. 1998. Implementation of Boolean function on one neural element and adders by mod 2. Lviv: Information technologies and systems. Vol. 1, Nr 1/2, 105-109. (in Russian).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c670128f-ad35-40ad-9c32-cd71f774c87d