Паралельне фільтрування рангу на основі імпульсної нейронної мережі типу “K-WINNERS-TAKE-ALL”
| dc.citation.epage | 165 | |
| dc.citation.issue | 881 | |
| dc.citation.journalTitle | Вісник Національного університету «Львівська політехніка». Серія: Комп’ютерні системи та мережі | |
| dc.citation.spage | 160 | |
| dc.contributor.affiliation | Національний університет “Львівська політехніка” | |
| dc.contributor.affiliation | Lviv Polytechnic National University | |
| dc.contributor.author | Тимощук, П. В. | |
| dc.contributor.author | Tymoshchuk, P. V. | |
| dc.coverage.placename | Львів | |
| dc.date.accessioned | 2018-09-25T08:55:57Z | |
| dc.date.available | 2018-09-25T08:55:57Z | |
| dc.date.created | 2017-03-28 | |
| dc.date.issued | 2017-03-28 | |
| dc.description.abstract | Представлено нейронну мережу (НМ) неперервного часу типу “K-winners-take-all” (KWTA), яка ідентифікує К найбільші з-поміж N входів, де керуючий сигнал 1 £ K < N . Мережа описується рівнянням стану з розривною правою частиною і вихідним рівнянням. Рівняння стану містить шлейф імпульсів, які описуються сумою дельта- функцій Дірака. Головною перевагою мережі порівняно з іншими близькими аналогами є відсутність обмежень на швидкість збіжності. Описано застосування мережі для паралельного фільтрування рангу. Отримані теоретичні результати проілюстровано прикладом комп’ютерного моделювання, який демонструє ефективність мережі. | |
| dc.description.abstract | A continuous-time K-winners-take-all (KWTA) neural network (NN) which is capable of identifying the largest K of N inputs, where a command signal 1 £ K < N has presented. The network is described by a state equation with a discontinuous right-hand side and by an output equation. The state equation contains an impulse train defined by a sum of Dirac delta functions. The main advantage of the network is not subject to the intrinsic convergence speed limitations of comparable designs. Application of the network for parallel rank-order filtering has described. Theoretical results are derived and illustrated with computer simulation example that demonstrates the network’s performance. | |
| dc.format.extent | 160-165 | |
| dc.format.pages | 6 | |
| dc.identifier.citation | Тимощук П. В. Паралельне фільтрування рангу на основі імпульсної нейронної мережі типу “K-WINNERS-TAKE-ALL” / П. В. Тимощук // Вісник Національного університету «Львівська політехніка». Серія: Комп’ютерні системи та мережі. — Львів : Видавництво Львівської політехніки, 2017. — № 881. — С. 160–165. | |
| dc.identifier.citationen | Tymoshchuk P. V. Parallel rank-order filtering based on impulse K-WINNERS-TAKE-ALL neural network / P. V. Tymoshchuk // Visnyk Natsionalnoho universytetu "Lvivska politekhnika". Serie: Kompiuterni systemy ta merezhi. — Lviv : Vydavnytstvo Lvivskoi politekhniky, 2017. — No 881. — P. 160–165. | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/42836 | |
| dc.language.iso | uk | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.relation.ispartof | Вісник Національного університету «Львівська політехніка». Серія: Комп’ютерні системи та мережі, 881, 2017 | |
| dc.relation.references | 1. Majani E., Erlanson R., and Abu-Mostafa Y. On the k-winners-take-all network Ґ// in Advances in Neural Information Processing Systems 1, R. P. Lippmann, J. E. Moody, and D. S. Touretzky, Eds. San Mateo, CA: Morgan Kaufmann, 1989, pp. 634–642. | |
| dc.relation.references | 2. Tymoshchuk P. A dynamic K-winners take all analog neural circuit // in Proc. IV th Int. Conf. “Perspective technologies and methods in MEMS design”, Lviv-Polyana, Ukraine, 2008, pp. 13–18. | |
| dc.relation.references | 3. Wang J. Analysis and design of a k-winners-take-all network with a single state variable and the Heaviside step activation function // IEEE Trans. Neural Netw., vol. 21, no. 9, P. 1496–1506, Sept. 2010. | |
| dc.relation.references | 4. Lippmann R. P. An introduction to computing with neural nets // IEEE Acoustics, Speech and Signal Processing Magazine, vol. 3, no. 4, pp. 4–22, Apr. 1987. | |
| dc.relation.references | 5. Tymoshchuk P. and Kaszkurewicz E. A winner-take all circuit using neural networks as building blocks // Neurocomputing, vol. 64, pp. 375–396, Mar. 2005. | |
| dc.relation.references | 6. Wunsch D. C. The cellular simultaneous recurrent network adaptive critic design for the generalized maze problem has a simple closed-form solution // in Proc. Int. Joint Conf. Neural Netw., Jul. 2000, P. 79–82. | |
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| dc.relation.references | 9. Itti L., Koch C., and Niebur E. A network of saliency-based visual attention for rapid scene analysis // IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 11, P. 1254 – 1259,Nov. 1998. | |
| dc.relation.references | 10. Cilingiroglu U. and Dake T. L. E. Rank-order filter design with a sampled-analog multiplewinners-take-all core // IEEE J. Solid-State Circuits, vol. 37, no. 2, pp. 978-984, Aug. 2002. | |
| dc.relation.references | 11. Erlanson R. and Abu-Mostafa Y. Analog neural networks as decoders // in Advances in Neural Information Processing Systems, vol. 1, R. P. Lippmann, J. E. Moody, and D. S. Touretzky, Eds. San Mateo, CA: Morgan Kaufmann, 1991. | |
| dc.relation.references | 12. Fish A., Akselrod D., and Yadid-Pecht O. High precision image centroid computation via an adaptive k-winner-take-all circuit in conjunction with a dynamic element matching algorithm for star tracking applications // Analog Integrated Circuits and Signal Processing, vol. 39, no. 3, P. 251–266, Jun. 2004. | |
| dc.relation.references | 13. Jain B. J. and Wysotzki F. Central clustering of attributed graphs // Machine Learning, vol. 56, no. 1, pp. 169–207, Jul. 2004. | |
| dc.relation.references | 14. Chartier S., Giguere G., Langlois D. and Sioufi R. Bidirectional associative memories, self-organizing maps and k-winners-take-all; uniting feature extraction and topological principles // in Proc. Int. Joint Conf. Neural Netw., Jun. 2009, pp. 503–510. | |
| dc.relation.references | 15. G. N. DeSouza and A. C. Zak, “Vision for mobile robot navigation: a survey,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 2, р. 237–267, Feb. 2002. | |
| dc.relation.references | 16. O’Reilly R. C. and Munakata Y. Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. Cambridge, MA: MIT Press, 2000. | |
| dc.relation.references | 17. Lazzaro J., Ryckebusch S., Mahowald M. A., and Mead C. A. Winner-take-all networks of O (N) complexity // in Advances in Neural Information Processing Systems 1, R. P. Lippmann, J. E. Moody, and D. S. Touretzky, Eds. San Mateo, CA: Morgan Kaufmann,1989, pp. 703-711. | |
| dc.relation.references | 18. Sekerkiran B. and Cilingiroglu U. A CMOS K-winners-take-all circuits with 0(N) complexity // IEEE Trans. Circuits Syst. II, vol. 46, no. 1, р. 1–5, Jan. 1999. | |
| dc.relation.references | 19. Maass W. Neural computation with winner-take-all as the only nonlinear operation // in Advances in Information Processing Systems, vol. 12, S. A. Solla, T. K. Leen, and K.-R. Mueller, Eds. Cambridge, MA: MIT Press, 2000, pp.293–299. | |
| dc.relation.references | 20. Calvert B. D. and Marinov C. A. Another K-winners-take-all analog neural network // IEEE Trans. Neural Netw., vol. 4, no. 1, P. 829–838, Jul. 2000. | |
| dc.relation.references | 21. Wang J. Analogue winner-take-all neural networks for determining maximum and minimum signals,” Int. J. Electron., vol. 77, no. 3, р. 355–367,Mar. 1994. | |
| dc.relation.references | 22. Cichocki A. and Unbehauen R. Neural Networks for Optimization and Signal Processing. New York, NY, USA: Wiley, 1993. | |
| dc.relation.referencesen | 1. Majani E., Erlanson R., and Abu-Mostafa Y. On the k-winners-take-all network G// in Advances in Neural Information Processing Systems 1, R. P. Lippmann, J. E. Moody, and D. S. Touretzky, Eds. San Mateo, CA: Morgan Kaufmann, 1989, pp. 634–642. | |
| dc.relation.referencesen | 2. Tymoshchuk P. A dynamic K-winners take all analog neural circuit, in Proc. IV th Int. Conf. "Perspective technologies and methods in MEMS design", Lviv-Polyana, Ukraine, 2008, pp. 13–18. | |
| dc.relation.referencesen | 3. Wang J. Analysis and design of a k-winners-take-all network with a single state variable and the Heaviside step activation function, IEEE Trans. Neural Netw., vol. 21, no. 9, P. 1496–1506, Sept. 2010. | |
| dc.relation.referencesen | 4. Lippmann R. P. An introduction to computing with neural nets, IEEE Acoustics, Speech and Signal Processing Magazine, vol. 3, no. 4, pp. 4–22, Apr. 1987. | |
| dc.relation.referencesen | 5. Tymoshchuk P. and Kaszkurewicz E. A winner-take all circuit using neural networks as building blocks, Neurocomputing, vol. 64, pp. 375–396, Mar. 2005. | |
| dc.relation.referencesen | 6. Wunsch D. C. The cellular simultaneous recurrent network adaptive critic design for the generalized maze problem has a simple closed-form solution, in Proc. Int. Joint Conf. Neural Netw., Jul. 2000, P. 79–82. | |
| dc.relation.referencesen | 7. Atkins M. Sorting by Hopfield nets, in Proc. Int. Joint Conf. Neural Netw., Jun. 1989, P. 65–68. | |
| dc.relation.referencesen | 8. Binh L. N. and Chong H. C. A neural-network contention controller for packet switching networks, IEEE Trans. Neural Netw. vol. 6, no. 6, P. 1402–1410, Nov. 1995. | |
| dc.relation.referencesen | 9. Itti L., Koch C., and Niebur E. A network of saliency-based visual attention for rapid scene analysis, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 11, P. 1254 – 1259,Nov. 1998. | |
| dc.relation.referencesen | 10. Cilingiroglu U. and Dake T. L. E. Rank-order filter design with a sampled-analog multiplewinners-take-all core, IEEE J. Solid-State Circuits, vol. 37, no. 2, pp. 978-984, Aug. 2002. | |
| dc.relation.referencesen | 11. Erlanson R. and Abu-Mostafa Y. Analog neural networks as decoders, in Advances in Neural Information Processing Systems, vol. 1, R. P. Lippmann, J. E. Moody, and D. S. Touretzky, Eds. San Mateo, CA: Morgan Kaufmann, 1991. | |
| dc.relation.referencesen | 12. Fish A., Akselrod D., and Yadid-Pecht O. High precision image centroid computation via an adaptive k-winner-take-all circuit in conjunction with a dynamic element matching algorithm for star tracking applications, Analog Integrated Circuits and Signal Processing, vol. 39, no. 3, P. 251–266, Jun. 2004. | |
| dc.relation.referencesen | 13. Jain B. J. and Wysotzki F. Central clustering of attributed graphs, Machine Learning, vol. 56, no. 1, pp. 169–207, Jul. 2004. | |
| dc.relation.referencesen | 14. Chartier S., Giguere G., Langlois D. and Sioufi R. Bidirectional associative memories, self-organizing maps and k-winners-take-all; uniting feature extraction and topological principles, in Proc. Int. Joint Conf. Neural Netw., Jun. 2009, pp. 503–510. | |
| dc.relation.referencesen | 15. G. N. DeSouza and A. C. Zak, "Vision for mobile robot navigation: a survey," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 2, r. 237–267, Feb. 2002. | |
| dc.relation.referencesen | 16. O’Reilly R. C. and Munakata Y. Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. Cambridge, MA: MIT Press, 2000. | |
| dc.relation.referencesen | 17. Lazzaro J., Ryckebusch S., Mahowald M. A., and Mead C. A. Winner-take-all networks of O (N) complexity, in Advances in Neural Information Processing Systems 1, R. P. Lippmann, J. E. Moody, and D. S. Touretzky, Eds. San Mateo, CA: Morgan Kaufmann,1989, pp. 703-711. | |
| dc.relation.referencesen | 18. Sekerkiran B. and Cilingiroglu U. A CMOS K-winners-take-all circuits with 0(N) complexity, IEEE Trans. Circuits Syst. II, vol. 46, no. 1, r. 1–5, Jan. 1999. | |
| dc.relation.referencesen | 19. Maass W. Neural computation with winner-take-all as the only nonlinear operation, in Advances in Information Processing Systems, vol. 12, S. A. Solla, T. K. Leen, and K.-R. Mueller, Eds. Cambridge, MA: MIT Press, 2000, pp.293–299. | |
| dc.relation.referencesen | 20. Calvert B. D. and Marinov C. A. Another K-winners-take-all analog neural network, IEEE Trans. Neural Netw., vol. 4, no. 1, P. 829–838, Jul. 2000. | |
| dc.relation.referencesen | 21. Wang J. Analogue winner-take-all neural networks for determining maximum and minimum signals," Int. J. Electron., vol. 77, no. 3, r. 355–367,Mar. 1994. | |
| dc.relation.referencesen | 22. Cichocki A. and Unbehauen R. Neural Networks for Optimization and Signal Processing. New York, NY, USA: Wiley, 1993. | |
| dc.rights.holder | © Національний університет „Львівська політехніка“, 2017 | |
| dc.rights.holder | © Тимощук П. В., 2017 | |
| dc.subject | мережа неперервного часу | |
| dc.subject | нейронна мережа (НМ) типу “K-winners-take-all” (KWTA) | |
| dc.subject | рівняння стану з розривною правою частиною | |
| dc.subject | шлейф імпульсів | |
| dc.subject | дельта-функція Дірака | |
| dc.subject | паралельне фільтрування рангу | |
| dc.subject | continuous-time network | |
| dc.subject | K-winners-take-all (KWTA) neural network (NN) | |
| dc.subject | state equation with a discontinuous right-hand side | |
| dc.subject | impulse train | |
| dc.subject | Dirac delta function | |
| dc.subject | parallel rank-order filtering | |
| dc.subject.udc | 004.032.026 | |
| dc.title | Паралельне фільтрування рангу на основі імпульсної нейронної мережі типу “K-WINNERS-TAKE-ALL” | |
| dc.title.alternative | Parallel rank-order filtering based on impulse K-WINNERS-TAKE-ALL neural network | |
| dc.type | Article |
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