| DC Field | Value | Language |
|---|
| dc.contributor.author | Vynokurova, Olena | |
| dc.contributor.author | Bodyanskiy, Yevgeniy | |
| dc.contributor.author | Peleshko, Dmytro | |
| dc.contributor.author | Rashkevych, Yuriy | |
| dc.coverage.temporal | 21-25 August 2018, Lviv | |
| dc.date.accessioned | 2020-06-19T12:04:55Z | - |
| dc.date.available | 2020-06-19T12:04:55Z | - |
| dc.date.created | 2018-02-28 | |
| dc.date.issued | 2018-02-28 | |
| dc.identifier.citation | The Autoencoder Based on Generalized Neo-Fuzzy Neuron and its Fast Learning for Deep Neural Networks / Olena Vynokurova, Yevgeniy Bodyanskiy, Dmytro Peleshko, Yuriy Rashkevych // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 113–118. — (Dynamic Data Mining & Data Stream Mining). | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/52472 | - |
| dc.description.abstract | In this paper the autoencoder based on the
generalized neo-fuzzy neurons is proposed. Also its fast
learning algorithm based on quadratic criterion was proposed.
Such system can be used as part of deep learning systems. The
proposed autoencoder is characterized by high learning speed
and less number of tuned parameters in comparison with wellknown autoencoders of “bottle neck” type. The efficiency of
proposed approach has been justified based on different
benchmarks and real data sets. | |
| dc.format.extent | 113-118 | |
| dc.language.iso | en | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 | |
| dc.relation.uri | http://www.ics.uci.edu/~mlearn/MLRepository.html | |
| dc.subject | autoencoder | |
| dc.subject | deep learning network | |
| dc.subject | neo-fuzzy neuron | |
| dc.subject | fast learning algorithm | |
| dc.subject | data compression | |
| dc.title | The Autoencoder Based on Generalized Neo-Fuzzy Neuron and its Fast Learning for Deep Neural Networks | |
| dc.type | Conference Abstract | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2018 | |
| dc.contributor.affiliation | Kharkiv National University of Radio Electronics | |
| dc.contributor.affiliation | IT Step University | |
| dc.contributor.affiliation | Ministry of Education and Science of Ukraine | |
| dc.format.pages | 6 | |
| dc.identifier.citationen | The Autoencoder Based on Generalized Neo-Fuzzy Neuron and its Fast Learning for Deep Neural Networks / Olena Vynokurova, Yevgeniy Bodyanskiy, Dmytro Peleshko, Yuriy Rashkevych // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 113–118. — (Dynamic Data Mining & Data Stream Mining). | |
| dc.relation.references | [1] Y. LeCun, Y. Bengio, and G.E. Hinton, “Deep Learning”. Nature, 2015, v. 521, pp. 436-444. | |
| dc.relation.references | [2] D. Graupe, Deep Learning Neural Networks: Design and Case Studies. World Scientific Publishing Company, 2016. | |
| dc.relation.references | [3] J. Schmidhuber, “Deep learning in neural networks: An overview.” Neural Networks, 2015, v. 61, pp. 85-117. | |
| dc.relation.references | [4] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT Press, 2016. | |
| dc.relation.references | [5] T. Miki, and T. Yamakawa, “Analog implementation of neo-fuzzy neuron and its on-board learning.” In: Mastorakis NE (eds) Computational Intelligence and Application, Piraeus: WSES Press, 1999, pp. 144–149. | |
| dc.relation.references | [6] T. Yamakawa, E. Uchino, T. Miki, and H. Kusanagi, “A neo-fuzzy neuron and its applications to system identification and prediction of the system behavior.” In: Proceedings 2-nd International Conference on Fuzzy Logic and Neural Networks (IIZUKA-92), Iizuka, Japan, 17-22 July 1992, pp. 477–483. | |
| dc.relation.references | [7] E. Uchino, and T. Yamakawa, “Soft computing based signal prediction, restoration and filtering.” In: Ruan D. (eds) Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks and Genetic Algorithms, Boston: Kluwer Academic Publishers, 1997, pp. 331–349. | |
| dc.relation.references | [8] Ye. Bodyanskiy, I. Kokshenev, and V. Kolodyazhniy, “An adaptive learning algorithm for a neo-fuzzy neuron.” In: Proceedings of 3-rd International Conference of European Union Society for Fuzzy Logic and Technology (EUSFLAT), Zittau, Germany, 10-12 September 2003, pp. 375–379 | |
| dc.relation.references | [9] V. Kolodyazhniy and Ye. Bodyanskiy, “Fuzzy Kolmogorov’s Network,” in Lecture Notes in Computer Science, vol. 3214, M.G. Negoita et al., Eds., Springer-Verlag, 2004, pp.764-771. | |
| dc.relation.references | [10] Ye. Bodyanskiy, V. Kolodyazhniy and P. Otto, “Neuro-fuzzy Kolmogorov’s network for time-series prediction and pattern classification,” in Lecture Notes in Artificial Intelligence, vol. 3698, U. Furbach, Ed., Heidelberg: Springer –Verlag, 2005, pp. 191-202. | |
| dc.relation.references | [11] V. Kolodyazhniy, Ye. Bodyanskiy and P. Otto, “Universal approximator employing neo-fuzzy neurons,” in Computational Intelligence Theory and Applications, Ed. B. Reusch, Ed., BerlinHeidelberg: Springer, 2005, pp. 631-640. | |
| dc.relation.references | [12] V. Kolodyazhniy, Ye. Bodyanskiy, V. Poyedyntseva, and A. Stephan “Neuro-fuzzy Kolmogorov’s network with a modified perceptron learning rule for classification problems,” in Advances in Soft Computing, vol. 38, B. Reuch, Ed., Berlin-Heidelberg: SpringerVerlag, 2006, pp. 41-49. | |
| dc.relation.references | [13] Ye. Bodyanskiy, Ye. Gorshkov, V. Kolodyazhniy, and V. Poyedyntseva “Neuro-fuzzy Kolmogorov's network,” in Lecture Notes in Computer Science, vol.3697, W. Duch, J. Kacprzyk, E. Oja, and S. Zadrozny, Eds.,Berlin-Heidelberg: Springer-Verlag, 2005, pp.1-6. | |
| dc.relation.references | [14] V. Kolodyazhniy, F. Klawonn, and K. Tschumitschew, “A neurofuzzy model for dimensionality reduction and its application” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems vol. 15, is. 05, October 2007, pp. 571-593. | |
| dc.relation.references | [15] Ye. Bodyanskiy, I. Pliss, and O. Vynokurova, “On-line neuro-fuzzy big data autoencoder for deep neural networks and its rapid learning” In: Proc. of XXX Int. Conference Problems of Decision making under uncertainties (PDMU 2017) August 14-19, 2017, Vilnius, Lithuania, P. 20. | |
| dc.relation.references | [16] Ye. Bodyanskiy, I. Pliss, D. Peleshko, Yu. Rashkevych, and O. Vynokurova, “Hybrid Generalized Additive Wavelet-Neuro-FuzzySystem and its Adaptive Learning”. In: Eds. Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J., Dependability Engineering and Complex Systems: Proceedings of the Eleventh International Conference on Dependability and Complex Systems DepCoS-RELCOMEX. June 27-July 1, 2016, Brunow, Poland, 2016, pp. 51-61. | |
| dc.relation.references | [17] O. Vynokurova, Ye. Bodyanskiy, I. Pliss, D. Peleshko, and Yu. Rashkevych. “Neo-fuzzy encoder and its adaptive learning for Big Data processing.” Scientific Journal of RTU, Series “Computer Science” Volume “Information Technology and Management Science” 2017, vol. 20, pp. 6–11. | |
| dc.relation.references | [18] R.P.Landim, B. Rodrigues, S.R. Silva, and W.M. Caminhas, “A neofuzzy-neuron with real time training applied to flux observer for an induction motor”. In: Proceedings of IEEE Vth Brazilian Symposium on Neural Networks, Belo Horizonte, 9-11 Dec 1998, pp. 67-72. | |
| dc.relation.references | [19] Ye. Bodyanskiy, D. Peleshko, I. Pliss, and O. Vynokurova. Hybrid adaptive systems of computational intelligence and their on-line learning in IT energy management tasks Green IT Engineering: Concepts, Models, Complex Systems Architectures, Eds. By V. Kharchenko, Yu. P Kondratenko, J. Kacprzyk, Series: Studies in Systems, Decision and Control, Book 74, Publisher: Springer; 2017, pp 229-244. | |
| dc.relation.references | [20] UCI Repository of machine learning databases. CA: University of California, Department of Information and Computer Science. [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html | |
| dc.relation.referencesen | [1] Y. LeCun, Y. Bengio, and G.E. Hinton, "Deep Learning". Nature, 2015, v. 521, pp. 436-444. | |
| dc.relation.referencesen | [2] D. Graupe, Deep Learning Neural Networks: Design and Case Studies. World Scientific Publishing Company, 2016. | |
| dc.relation.referencesen | [3] J. Schmidhuber, "Deep learning in neural networks: An overview." Neural Networks, 2015, v. 61, pp. 85-117. | |
| dc.relation.referencesen | [4] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT Press, 2016. | |
| dc.relation.referencesen | [5] T. Miki, and T. Yamakawa, "Analog implementation of neo-fuzzy neuron and its on-board learning." In: Mastorakis NE (eds) Computational Intelligence and Application, Piraeus: WSES Press, 1999, pp. 144–149. | |
| dc.relation.referencesen | [6] T. Yamakawa, E. Uchino, T. Miki, and H. Kusanagi, "A neo-fuzzy neuron and its applications to system identification and prediction of the system behavior." In: Proceedings 2-nd International Conference on Fuzzy Logic and Neural Networks (IIZUKA-92), Iizuka, Japan, 17-22 July 1992, pp. 477–483. | |
| dc.relation.referencesen | [7] E. Uchino, and T. Yamakawa, "Soft computing based signal prediction, restoration and filtering." In: Ruan D. (eds) Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks and Genetic Algorithms, Boston: Kluwer Academic Publishers, 1997, pp. 331–349. | |
| dc.relation.referencesen | [8] Ye. Bodyanskiy, I. Kokshenev, and V. Kolodyazhniy, "An adaptive learning algorithm for a neo-fuzzy neuron." In: Proceedings of 3-rd International Conference of European Union Society for Fuzzy Logic and Technology (EUSFLAT), Zittau, Germany, 10-12 September 2003, pp. 375–379 | |
| dc.relation.referencesen | [9] V. Kolodyazhniy and Ye. Bodyanskiy, "Fuzzy Kolmogorov’s Network," in Lecture Notes in Computer Science, vol. 3214, M.G. Negoita et al., Eds., Springer-Verlag, 2004, pp.764-771. | |
| dc.relation.referencesen | [10] Ye. Bodyanskiy, V. Kolodyazhniy and P. Otto, "Neuro-fuzzy Kolmogorov’s network for time-series prediction and pattern classification," in Lecture Notes in Artificial Intelligence, vol. 3698, U. Furbach, Ed., Heidelberg: Springer –Verlag, 2005, pp. 191-202. | |
| dc.relation.referencesen | [11] V. Kolodyazhniy, Ye. Bodyanskiy and P. Otto, "Universal approximator employing neo-fuzzy neurons," in Computational Intelligence Theory and Applications, Ed. B. Reusch, Ed., BerlinHeidelberg: Springer, 2005, pp. 631-640. | |
| dc.relation.referencesen | [12] V. Kolodyazhniy, Ye. Bodyanskiy, V. Poyedyntseva, and A. Stephan "Neuro-fuzzy Kolmogorov’s network with a modified perceptron learning rule for classification problems," in Advances in Soft Computing, vol. 38, B. Reuch, Ed., Berlin-Heidelberg: SpringerVerlag, 2006, pp. 41-49. | |
| dc.relation.referencesen | [13] Ye. Bodyanskiy, Ye. Gorshkov, V. Kolodyazhniy, and V. Poyedyntseva "Neuro-fuzzy Kolmogorov's network," in Lecture Notes in Computer Science, vol.3697, W. Duch, J. Kacprzyk, E. Oja, and S. Zadrozny, Eds.,Berlin-Heidelberg: Springer-Verlag, 2005, pp.1-6. | |
| dc.relation.referencesen | [14] V. Kolodyazhniy, F. Klawonn, and K. Tschumitschew, "A neurofuzzy model for dimensionality reduction and its application" International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems vol. 15, is. 05, October 2007, pp. 571-593. | |
| dc.relation.referencesen | [15] Ye. Bodyanskiy, I. Pliss, and O. Vynokurova, "On-line neuro-fuzzy big data autoencoder for deep neural networks and its rapid learning" In: Proc. of XXX Int. Conference Problems of Decision making under uncertainties (PDMU 2017) August 14-19, 2017, Vilnius, Lithuania, P. 20. | |
| dc.relation.referencesen | [16] Ye. Bodyanskiy, I. Pliss, D. Peleshko, Yu. Rashkevych, and O. Vynokurova, "Hybrid Generalized Additive Wavelet-Neuro-FuzzySystem and its Adaptive Learning". In: Eds. Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J., Dependability Engineering and Complex Systems: Proceedings of the Eleventh International Conference on Dependability and Complex Systems DepCoS-RELCOMEX. June 27-July 1, 2016, Brunow, Poland, 2016, pp. 51-61. | |
| dc.relation.referencesen | [17] O. Vynokurova, Ye. Bodyanskiy, I. Pliss, D. Peleshko, and Yu. Rashkevych. "Neo-fuzzy encoder and its adaptive learning for Big Data processing." Scientific Journal of RTU, Series "Computer Science" Volume "Information Technology and Management Science" 2017, vol. 20, pp. 6–11. | |
| dc.relation.referencesen | [18] R.P.Landim, B. Rodrigues, S.R. Silva, and W.M. Caminhas, "A neofuzzy-neuron with real time training applied to flux observer for an induction motor". In: Proceedings of IEEE Vth Brazilian Symposium on Neural Networks, Belo Horizonte, 9-11 Dec 1998, pp. 67-72. | |
| dc.relation.referencesen | [19] Ye. Bodyanskiy, D. Peleshko, I. Pliss, and O. Vynokurova. Hybrid adaptive systems of computational intelligence and their on-line learning in IT energy management tasks Green IT Engineering: Concepts, Models, Complex Systems Architectures, Eds. By V. Kharchenko, Yu. P Kondratenko, J. Kacprzyk, Series: Studies in Systems, Decision and Control, Book 74, Publisher: Springer; 2017, pp 229-244. | |
| dc.relation.referencesen | [20] UCI Repository of machine learning databases. CA: University of California, Department of Information and Computer Science. [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html | |
| dc.citation.conference | IEEE second international conference "Data stream mining and processing" | |
| dc.citation.spage | 113 | |
| dc.citation.epage | 118 | |
| dc.coverage.placename | Львів | |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference
|
