A modified neural network for antennas optimization

• Autorzy: Costa Marcos

Identyfikatory

DOI

10.15199/48.2020.11.21

Warianty tytułu

PL

Zmodyfikowana sieć neuronowa do projektowania i optymalizacji anten

• Języki publikacji: EN

Abstrakty

EN

In mobile communications, devices are generally more compact, nevertheless allow data traffic at high speeds. To meet such demand, embedded hardware must present limited dimensions and at the same time be robust enough to ensure high communication speeds. In this work, a modified Hopfield neural network was applied in the optimization of planar antennas. The role of the algorithm presented here, is to find the ideal antenna dimensions to meet the future 5G mobile technology. With this, a significant improvement in resonance, gain and directivity was expected, which are some of the important parameters in antenna analysis. In the literature, no reference was found based on the modified Hopfield neural network applied to the optimization of planar antennas, which further enhances this research, providing an important and unprecedented contribution. The analysis of the results shows the efficiency, robustness, precision and reliability of this approach, encouraging further research in this area.

PL

W pracy przedstawiono zmodyfikowaną sieć neuronową Hopfielda wykorzystaną do optymalizacji planarnej anteny. Rolą algorytmu jest znalezienie wymiarów anteny tak aby można było projektować anteny 5G. Doatkowo można antenę optymalizować pod kątem wzmocniania w rezonansie i kierunkowości.

Słowa kluczowe

EN

planar antennas; 5G; artificial neural networks; hopfield networks; antenna optimization

PL

antena planarna; sieć 5G; sieć neuronowa; optymalizacja

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2020
• Tom: R. 96, nr 11
• Strony: 104--108
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Costa Marcos

• Faculdade de Engenharia Elétrica, Universidade Federal Tocantins, Av: NS 15 ALC NO 14, 109 Norte - 77001-090, Palmas - TO, Brazil

Bibliografia

• [1] Pierucci, L. (2015) The Quality of Experience Perspective Toward 5G Technology. Proceedingsof IEEE Wireless Communications, 4(22); pp. 10-16.
• [2] Osama, M. H., Ayman, E. and Abdel-Razik S. (2014) New dense dieletric patch array antenna for future 5G short-range communications. Proceedings in 16th International Symposium on Antenna Technology and Applied Electromagnetics(ANTEM), Victoria, BC, Canada. July 13-16; pp 1-4.
• [3] Kabalci Y. (2019) 5G Mobile Communication Systems: Fundamentals, Challenges, and Key Technologies. In: Kabalci E., Kabalci Y. (eds) Smart Grids and Their Communication Systems. Energy Systems in Electrical Engineering. Springer, Singapore, pp. 329-359
• [4] Fujimoto, K. and James, H., (1987) Small Antennas. John Wiley & Sons:Research Studies Press, United Kingdom.
• [5] Zhai, W., Miraftab, V., Repeta, M. (2015) Broadband antenna array with low cost PCB substrate for 5G millimeter wave applications. Proceedings of Global Symposium on Millimeter- Waves (GSMM), May 25-27; pp 978-980.
• [6] Garg, R. and Bahl, I. (2001) Microstrip Antenna Desing HandBook. Artech House, Norwood, United Kingdom.
• [7] Chen, Z. N. andChia, M. Y. (2006) Broadband Planar Antennas: design and applications.John Wiley & Sons, Chichester, United Kingdom.
• [8] Wong, K. L. (2002) Compact and Broadband Microstrip Antennas. John Wiley & Sons, Inc. New York, United States of America.
• [9] Braga, A.P.,Carvalho, A. C. P. L. F. andLudemir, T. B. (2007) Redes Neurais Artificiais: teoria e aplicações. 2nded. Rio de Janeiro: LTC, Brazil.
• [10] Fujimoto, K. and Morishita, H. (2013) Modern Small Antennas. Vol. 1, Cambrige University Press, New York, United States of America.
• [11] Mandal D., Kar R., Bandyopadhyay S. (2019) RGA-Based Wide Null Control for Compact Linear Antenna Array. In: Bhatia S., Tiwari S., Mishra K., Trivedi M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing. Springer, Singapore. 759, pp. 269-280.
• [12] Balanis, C. A. (2016) Antenna theory: analysis and design.4thed. New Jersey: John Wiley& Sons, Inc. United States of America.
• [13] Boccardi, F., Heath, R. W., Lozano, A., Marzetta, T. L. and Popovski, P. (2014) Five Disruptive Technology Directions for 5G. Proceedings of IEEE Communications Magazine, 2(52). February; pp 74-80.
• [14] Silva, I. N., Amaral, W. C. and Arruda, L. V. R. (2004) Uma abordagem usando redes neurais artificiais para resolução de problemas de otimização restrita. Pesquisa Operacional. 24(2), pp. 285-302.
• [15] Hopfield, J. J. (1982) Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences. 79, pp. 2554-2558.
• [16] Hopfield, J. J. and Tank, D. W. (1985). Neural computation of decisions in optimization problems. In Biological Cybernetics. 52(3), pp. 141-152.
• [17] Tank, D. W. and J. J. Hopfield (1986). Simple neural optimization networks: an A/D converter, signal decision network and a linear pro-gramming circuit.In IEEE Transaction on Circuits and Systems. 33(5) pp. 533-541.
• [18] Silva, I. N., Goedtel, A. and Flauzino, R.A., (2007) The Modified Hopfiled Architecture Applied in Dynamic Programming Problems and Bipartite Graph Optimization. International Journal of Hybrid Intelligent Systems 4, pp. 17-26.
• [19] Aiyer, S.V.B., Niranjan, M. and Fallside, F. (1990) A theoretical investigation into the performance of the hopfield network.In IEEE Transaction on Neural Networks. 1(2), pp. 204-215.
• [20] Silva, I. N., Souza, A. N. and Ulson, J. A. C. (2001) A Modified Hopfield Model for Solving Several Types of Optimization Problems. In: Cihan H. Dagli; Anna L. Buczak. (Org.). Intelligent Engineering Systems ThroughArtificial Neural Networks. 1st ed. New York, USA: ASME Press. 11, pp. 897- 902.
• [21] Silva, I. N., Amaral, W. C., and Arruda, L. V. R. (2005). Design and analysis of efficient neural network model for solving nonlinear optimization problems, International Journal of Systems Science. 28(13), pp. 833-843.
• [22] Liang, X. B. andWang, J.,(2000) A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints. In IEEE Transactions on Neural Networks 11(6)pp 1251 – 1262.
• [23] Medeiros, T. E. de L., (2013) Antenas de microfita sobre substrato dielétrico organizado de forma quase periódica. Masters dissertation in Communication and Automation Systems. Federal Rural University of the Semi-Arid (UFERSA), Mossoró (RN) Brazil.
• [24] Kennedy, M. P. and Chua, L. O. (1988) Neural Networks for Nonlinear Programming. In IEEE Transaction Circuits Systems.35(5) pp 554-562.
• [25] Vidyasagar, M. (2002) Nonlinear Systems Analysis: Second Edition. Prentice-Hall, Englewood Cliffs (NJ), United States of America.
• [26] Luenberger, D. G. (1984) Linear and Nonlinear Programming. 2nd ed: Addison-Wesley.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).