COMPUTATIONAL EFFICIENCY OF AN ARTIFICIAL NEURAL NETWORK IN OPTIMIZING THE OPERATING CONDITIONS OF PATCH ANTENNAS
Subject and Purpose. Designing antennas with desired operational features is a complex optimization problem addressing a large number of parameters and nonlinear relationships. Artificial neural networks (ANNs) have had strong potential in antenna engineering. The ability to approximate nonlinear fu...
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Radio physics and radio astronomy| _version_ | 1868203884978110464 |
|---|---|
| author | Butov, S. Tuz, A. Morozova, O. Vinnichenko, S. Khardikov, O. Shulga, S. |
| author_facet | Butov, S. Tuz, A. Morozova, O. Vinnichenko, S. Khardikov, O. Shulga, S. |
| author_institution_txt_mv | [] |
| author_sort | Butov, S. |
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| collection | OJS |
| datestamp_date | 2026-06-16T11:44:37Z |
| description | Subject and Purpose. Designing antennas with desired operational features is a complex optimization problem addressing a large number of parameters and nonlinear relationships. Artificial neural networks (ANNs) have had strong potential in antenna engineering. The ability to approximate nonlinear functions and capture hidden relationships enables partial automation of antenna designing. However, it requires a detailed analysis of how ANN parameters affect predictive accuracy and computational efficiency. The present study investigates the influence of ANN architecture and training configurations on the predictive accuracy of resonant frequencies for rectangular microstrip patch antennas.Methods and Methodology. A modular Python-based experimental platform is used to evaluate ANN performance for dif- ferent ANN configurations. The ANN training is on a synthetic dataset generated from a classical analytical antenna model. The number of hidden layers, neurons per layer, activation functions, and optimizers are systematically varied to assess their particular impacts on convergence, generalization performance, and execution time.Results. It has been shown that a three-layer neural network [1024, 512, 256 neurons] with ReLU activation and the Adam optimizer strikes the best balance between predictive accuracy and training rate. Simpler or excessively deep architectures, non-adaptive optimizers, and saturating activation functions can slow convergence or cause unstable training. Further analysis indicated that a smaller batch size introduces useful stochasticity into training but might also destabilize the process.Conclusions. The study has demonstrated that joint optimization of ANN architecture and training dynamics is essential for developing accurate and computationally efficient electromagnetic models. The practical results are recommendations for machine-learning-based antenna design.Keywords: artificial neural network (ANN), activation function, adaptive optimizer, microstrip patch antenna, automatic antenna designManuscript submitted 17.10.2025Radio phys. radio astron. 2026, 31(2): 098-107REFERENCES1. James, J.R., Hall, P.S., Wood, C., 1981. Microstrip Antenna. Theory and Design, London: Peter Peregrinus LTD.2. Kumar, G., Kamala, P.R., 2003. Broadband Microstrip Antennas. Boston, London: Artech House.3. Waterhouse, R.B., 2013, Microstrip Patch Antennas: A Designer’s Guide. New York: Springer Science & Business Media.4. Patnaik, A., Anagnostou, D.E., Mishra, R.K., Christodoulou, C.G., Lyke, J.C., 2004. Applications of neural networks in wire- less communications, IEEE Antennas and Propagation Magazine, 46(3), pp. 130—137. DOI: 10.1109/MAP.2004.13741255. Feng, F., Zhang, J., Na, W., Zhang, W., Jin, J., Zhang, Q.-J., 2022. Artificial neural networks for microwave computer-aided design: The state of the art. IEEE Trans. Microw. Theory Tech., 70(11), pp. 4597—4619. DOI: 10.1109/TMTT.2022.31977516. Yu, Y., Zhang, Z., Cheng, Q.S., Liu, B., Wang, Y., Guo, C., Ye, T.T., 2022. State-of-the-art: AI-assisted surrogate mod- eling and optimization for microwave filters. IEEE Trans. Microw. Theory Tech., 70(11), pp. 4635—4651. DOI:10.1109/ TMTT.2022.32088987. Qi, S., Sarris, C.D., 2022. Deep neural networks for rapid simulation of planar microwave circuits based on their layouts.IEEE Trans. Microw. Theory Tech., 70(11), pp. 4805—4815. DOI: 10.1109/TMTT.2022.32102298. Swaminathan, M., Bhatti, O.W., Guo, Y., Huang, E., Akinwande, O., 2022. Bayesian Learning for Uncertainty Quanti-fication, Optimization, and Inverse Design. IEEE Trans. Microw. Theory Tech., 70(11), pp. 4620—4634. DOI: 10.1109/TMTT.2022.32064559. Ma, J., Dang, S., Li, P., Watkins, G., Morris, K.A., Beach, M.A., 2023. A learning-based methodology for microwave passive component design. IEEE Trans. Microw. Theory Tech., 71(7), pp. 3037—3050. DOI: 10.1109/TMTT.2023.323841810. Thakare, V.V., Singhal, P., 2010. Microstrip antenna design using artificial neural networks. Int. J. RF Microw. Comput.-Aid-ed Eng., 20(1), pp. 76—86. DOI: 10.1002/mmce.2041411. Rawat, A., Yadav, R.N., Shrivastava, S.C., 2012. Neural network applications in smart antenna arrays: A review. AEU — Int. J. Electron. Commun., 66(11), pp. 903—912. DOI: 10.1016/j.aeue.2012.03.01212. Khan, T., De, A., 2015. Modeling of microstrip antennas using neural networks techniques: a review. Int. J. RF Microw. Comput.-Aided Eng., 25(9), pp. 747—757. DOI: 10.1002/mmce.2091013. Shi, D., Lian, W., Cui, K., Leong, D.S.H., 2022. An intelligent antenna synthesis method based on machine learning. IEEE Trans. Antennas Propag., 70(7), pp. 4965—4976. DOI: 10.1109/TAP.2022.318269314. Massa, A., Salucci, M., 2022. On the design of complex EM devices and systems through the system-by-design para-digm: A framework for dealing with the computational complexity. IEEE Trans. Antennas Propag., 70(2), pp. 1328—1343. DOI: 10.1109/TAP.2021.311141715. Raya, M.B., Pal, S., Ali, K., 2019. Design of inset fed rectangular shaped microstrip patch antenna using deep neural net-works. In: Proc. 2019 22nd Int. Conf. on Computer and Information Technology (ICCIT). Dhaka, Bangladesh, 18—20 Dec. 2019. Dhaka: IEEE, 2019. DOI: 10.1109/ICCIT48885.2019.903828416. Pal, S., Raya, M.B., Ali, K., 2019. Computation of resonant frequency and gain from inset fed rectangular shaped microstrippatch antenna using deep neural network. In: Proc. 2019 4th Int. Con. on Electrical Information and Communication Tech-nology (EICT). Khulna, Bangladesh, 20—22 Dec. 2019. Khulna. DOI: 10.1109/EICT48899.2019.906875817. Syahrial, S., Al Faqi, M.K., Meutia, E.D., Munadi, R., Roslidar R., 2024. Parameter estimation of two-element rectangular microstrip patch antenna using artificial neural network. In: Proc. 2024 FORTEI-Int. Conf. on Electrical Engineering (FORTEI-ICEE). Badung, Indonesia, 24—25 Oct. 2024. Badung: IEEE, pp. 216—221. DOI: 10.1109/FORTEI-ICEE64706.2024.1082453718. Shereen, M.K., Liu, X., Wu, X., Niazi, S., Naseem, A., Khattak, M.I., 2025. Towards bi-directional deep learning approach in patch antenna design and optimization. In: Proc. 2025 IEEE Int. Workshop on Antenna Technology (iWAT). Cocoa Beach, FL, USA. 19—21 Feb. 2025. Cocoa Beach: IEEE. DOI: 10.1109/iWAT64079.2025.1093121019. Derneryd, A., Lind, A., 1979. Extended analysis of rectangular microstrip resonator antennas. IEEE Trans. AntennasPropag., 27(6), pp. 846—849. DOI: 10.1109/TAP.1979.114220620. Garg, R., Bhartia, P., Bahl, I.J., Ittipiboon, A., 2001. Microstrip Antenna Design Handbook. Boston, London: Artech House.21. Steer, M., 2019. Fundamentals of Microwave and RF Design, 3rd ed. USA: NC State University.22. Fesenko, V.I., Tkachenko, G.V., 2007. Modeling of 1-D photonic bandgap microstrip structures. In: Proc. 2007 Int. Work-shop on Optoelectronic Physics and Technology (OPT’07), Kharkiv, Ukraine, 20—22 June 2007, pp. 42—43. DOI: 10.1109/OPT.2007.4298524.23. Mishra, R.K., Patnaik, A., 2003. Designing rectangular patch antenna using the neurospectral method. IEEE Trans. Anten-nas Propag., 51(8), pp. 1914—1921. DOI: 10.1109/TAP.2003.81474824. Haykin, S., 2009. Neural Networks and Learning Machines. 3rd ed. NJ: Pearson Education.25. Goodfellow, I., Bengio, Y., Courville, A., 2016. Deep Learning. Cambridge, Massachusetts London: MIT Press.26. He, K., Zhang, X., Ren, S., Sun, J., 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In: Proc. 2015 IEEE Int. Conf. on Computer Vision (ICCV). Santiago, Chile, 7—13 Dec. 2015, pp. 1026—1034. DOI: 10.1109/ICCV.2015.12327. Maity, B., Nayak, S.K., 2024. Artificial neural networks for optimal design parameters prediction of microstrip patch antenna with wideband harmonic suppression. In: Proc. 2024 Second Int. Conf. on Microwave, Antenna and Communication(MAC). Dehradun, India, 04—06 Oct. 2024. DOI: 10.1109/MAC61551.2024.10837513 |
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| institution | Radio physics and radio astronomy |
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| spelling | rpra-journalorgua-article-14942026-06-16T11:44:37Z COMPUTATIONAL EFFICIENCY OF AN ARTIFICIAL NEURAL NETWORK IN OPTIMIZING THE OPERATING CONDITIONS OF PATCH ANTENNAS ОБЧИСЛЮВАЛЬНА ЕФЕКТИВНІСТЬ ШТУЧНОЇ НЕЙРОННОЇ МЕРЕЖІ ДЛЯ ОПТИМІЗАЦІЇ УМОВ РОБОТИ МІКРОСМУЖКОВИХ АНТЕН Butov, S. Tuz, A. Morozova, O. Vinnichenko, S. Khardikov, O. Shulga, S. artificial neural network (ANN); activation function; adaptive optimizer; microstrip patch antenna; automatic antenna design штучна нейрона мережа; функція активації; адаптивний оптимізатор; мікросмужкова патч-антена; автоматичне проєктування антен Subject and Purpose. Designing antennas with desired operational features is a complex optimization problem addressing a large number of parameters and nonlinear relationships. Artificial neural networks (ANNs) have had strong potential in antenna engineering. The ability to approximate nonlinear functions and capture hidden relationships enables partial automation of antenna designing. However, it requires a detailed analysis of how ANN parameters affect predictive accuracy and computational efficiency. The present study investigates the influence of ANN architecture and training configurations on the predictive accuracy of resonant frequencies for rectangular microstrip patch antennas.Methods and Methodology. A modular Python-based experimental platform is used to evaluate ANN performance for dif- ferent ANN configurations. The ANN training is on a synthetic dataset generated from a classical analytical antenna model. The number of hidden layers, neurons per layer, activation functions, and optimizers are systematically varied to assess their particular impacts on convergence, generalization performance, and execution time.Results. It has been shown that a three-layer neural network [1024, 512, 256 neurons] with ReLU activation and the Adam optimizer strikes the best balance between predictive accuracy and training rate. Simpler or excessively deep architectures, non-adaptive optimizers, and saturating activation functions can slow convergence or cause unstable training. Further analysis indicated that a smaller batch size introduces useful stochasticity into training but might also destabilize the process.Conclusions. The study has demonstrated that joint optimization of ANN architecture and training dynamics is essential for developing accurate and computationally efficient electromagnetic models. The practical results are recommendations for machine-learning-based antenna design.Keywords: artificial neural network (ANN), activation function, adaptive optimizer, microstrip patch antenna, automatic antenna designManuscript submitted 17.10.2025Radio phys. radio astron. 2026, 31(2): 098-107REFERENCES1. James, J.R., Hall, P.S., Wood, C., 1981. Microstrip Antenna. Theory and Design, London: Peter Peregrinus LTD.2. Kumar, G., Kamala, P.R., 2003. Broadband Microstrip Antennas. Boston, London: Artech House.3. Waterhouse, R.B., 2013, Microstrip Patch Antennas: A Designer’s Guide. New York: Springer Science & Business Media.4. Patnaik, A., Anagnostou, D.E., Mishra, R.K., Christodoulou, C.G., Lyke, J.C., 2004. Applications of neural networks in wire- less communications, IEEE Antennas and Propagation Magazine, 46(3), pp. 130—137. DOI: 10.1109/MAP.2004.13741255. Feng, F., Zhang, J., Na, W., Zhang, W., Jin, J., Zhang, Q.-J., 2022. Artificial neural networks for microwave computer-aided design: The state of the art. IEEE Trans. Microw. Theory Tech., 70(11), pp. 4597—4619. DOI: 10.1109/TMTT.2022.31977516. Yu, Y., Zhang, Z., Cheng, Q.S., Liu, B., Wang, Y., Guo, C., Ye, T.T., 2022. State-of-the-art: AI-assisted surrogate mod- eling and optimization for microwave filters. IEEE Trans. Microw. Theory Tech., 70(11), pp. 4635—4651. DOI:10.1109/ TMTT.2022.32088987. Qi, S., Sarris, C.D., 2022. Deep neural networks for rapid simulation of planar microwave circuits based on their layouts.IEEE Trans. Microw. Theory Tech., 70(11), pp. 4805—4815. DOI: 10.1109/TMTT.2022.32102298. Swaminathan, M., Bhatti, O.W., Guo, Y., Huang, E., Akinwande, O., 2022. Bayesian Learning for Uncertainty Quanti-fication, Optimization, and Inverse Design. IEEE Trans. Microw. Theory Tech., 70(11), pp. 4620—4634. DOI: 10.1109/TMTT.2022.32064559. Ma, J., Dang, S., Li, P., Watkins, G., Morris, K.A., Beach, M.A., 2023. A learning-based methodology for microwave passive component design. IEEE Trans. Microw. Theory Tech., 71(7), pp. 3037—3050. DOI: 10.1109/TMTT.2023.323841810. Thakare, V.V., Singhal, P., 2010. Microstrip antenna design using artificial neural networks. Int. J. RF Microw. Comput.-Aid-ed Eng., 20(1), pp. 76—86. DOI: 10.1002/mmce.2041411. Rawat, A., Yadav, R.N., Shrivastava, S.C., 2012. Neural network applications in smart antenna arrays: A review. AEU — Int. J. Electron. Commun., 66(11), pp. 903—912. DOI: 10.1016/j.aeue.2012.03.01212. Khan, T., De, A., 2015. Modeling of microstrip antennas using neural networks techniques: a review. Int. J. RF Microw. Comput.-Aided Eng., 25(9), pp. 747—757. DOI: 10.1002/mmce.2091013. Shi, D., Lian, W., Cui, K., Leong, D.S.H., 2022. An intelligent antenna synthesis method based on machine learning. IEEE Trans. Antennas Propag., 70(7), pp. 4965—4976. DOI: 10.1109/TAP.2022.318269314. Massa, A., Salucci, M., 2022. On the design of complex EM devices and systems through the system-by-design para-digm: A framework for dealing with the computational complexity. IEEE Trans. Antennas Propag., 70(2), pp. 1328—1343. DOI: 10.1109/TAP.2021.311141715. Raya, M.B., Pal, S., Ali, K., 2019. Design of inset fed rectangular shaped microstrip patch antenna using deep neural net-works. In: Proc. 2019 22nd Int. Conf. on Computer and Information Technology (ICCIT). Dhaka, Bangladesh, 18—20 Dec. 2019. Dhaka: IEEE, 2019. DOI: 10.1109/ICCIT48885.2019.903828416. Pal, S., Raya, M.B., Ali, K., 2019. Computation of resonant frequency and gain from inset fed rectangular shaped microstrippatch antenna using deep neural network. In: Proc. 2019 4th Int. Con. on Electrical Information and Communication Tech-nology (EICT). Khulna, Bangladesh, 20—22 Dec. 2019. Khulna. DOI: 10.1109/EICT48899.2019.906875817. Syahrial, S., Al Faqi, M.K., Meutia, E.D., Munadi, R., Roslidar R., 2024. Parameter estimation of two-element rectangular microstrip patch antenna using artificial neural network. In: Proc. 2024 FORTEI-Int. Conf. on Electrical Engineering (FORTEI-ICEE). Badung, Indonesia, 24—25 Oct. 2024. Badung: IEEE, pp. 216—221. DOI: 10.1109/FORTEI-ICEE64706.2024.1082453718. Shereen, M.K., Liu, X., Wu, X., Niazi, S., Naseem, A., Khattak, M.I., 2025. Towards bi-directional deep learning approach in patch antenna design and optimization. In: Proc. 2025 IEEE Int. Workshop on Antenna Technology (iWAT). Cocoa Beach, FL, USA. 19—21 Feb. 2025. Cocoa Beach: IEEE. DOI: 10.1109/iWAT64079.2025.1093121019. Derneryd, A., Lind, A., 1979. Extended analysis of rectangular microstrip resonator antennas. IEEE Trans. AntennasPropag., 27(6), pp. 846—849. DOI: 10.1109/TAP.1979.114220620. Garg, R., Bhartia, P., Bahl, I.J., Ittipiboon, A., 2001. Microstrip Antenna Design Handbook. Boston, London: Artech House.21. Steer, M., 2019. Fundamentals of Microwave and RF Design, 3rd ed. USA: NC State University.22. Fesenko, V.I., Tkachenko, G.V., 2007. Modeling of 1-D photonic bandgap microstrip structures. In: Proc. 2007 Int. Work-shop on Optoelectronic Physics and Technology (OPT’07), Kharkiv, Ukraine, 20—22 June 2007, pp. 42—43. DOI: 10.1109/OPT.2007.4298524.23. Mishra, R.K., Patnaik, A., 2003. Designing rectangular patch antenna using the neurospectral method. IEEE Trans. Anten-nas Propag., 51(8), pp. 1914—1921. DOI: 10.1109/TAP.2003.81474824. Haykin, S., 2009. Neural Networks and Learning Machines. 3rd ed. NJ: Pearson Education.25. Goodfellow, I., Bengio, Y., Courville, A., 2016. Deep Learning. Cambridge, Massachusetts London: MIT Press.26. He, K., Zhang, X., Ren, S., Sun, J., 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In: Proc. 2015 IEEE Int. Conf. on Computer Vision (ICCV). Santiago, Chile, 7—13 Dec. 2015, pp. 1026—1034. DOI: 10.1109/ICCV.2015.12327. Maity, B., Nayak, S.K., 2024. Artificial neural networks for optimal design parameters prediction of microstrip patch antenna with wideband harmonic suppression. In: Proc. 2024 Second Int. Conf. on Microwave, Antenna and Communication(MAC). Dehradun, India, 04—06 Oct. 2024. DOI: 10.1109/MAC61551.2024.10837513 Предмет і мета роботи. Проєктування антен із заданими експлуатаційними характеристиками є складною задачею оптимізації з великою кількістю параметрів і нелінійних залежностей. Штучні нейронні мережі (ШНМ) продемонстрували значний потенціал в антенній інженерії завдяки здатності апроксимувати нелінійні функції та виявляти приховані взаємозв’язки, що забезпечує часткову автоматизацію процесу проєктування антен. Водночас це потребує детального аналізу впливу параметрів ШНМ на точність прогнозування та обчислювальну ефективність. Метою дослідження є вивчення впливу архітектури ШНМ і конфігурації навчання на точність передбачення резонансної частоти прямокутних мікросмужкових патч-антен.Методи та методологія. Для оцінювання ефективності ШНМ за різних конфігурацій було розроблено модульну експериментальну платформу на базі Python. Навчання мереж здійснювалося на синтетичному наборі даних, згене- рованому на основі класичної аналітичної моделі антени. Кількість прихованих шарів і нейронів у кожному шарі, типи функцій активації та оптимізатори систематично варіювалися з метою оцінювання їхнього впливу на збіжність, здатність до узагальнення та час виконання.Результати. Отримані результати свідчать, що тришарова нейронна мережа з конфігурацією [1024, 512, 256] ней- ронів, функцією активації ReLU та оптимізатором Adam забезпечує найкращий баланс між точністю прогнозування та швидкістю навчання. Простіші або надмірно глибокі архітектури, неадаптивні оптимізатори та насичувальні функції активації призводять до уповільненої збіжності або нестабільного навчання. Додатковий аналіз показав, що зменшення розміру пакету підвищує стохастичність процесу навчання, проте може знижувати його стабільність.Висновки. Дослідження демонструє, що спільна оптимізація архітектури ШНМ і динаміки процесу навчання є необхідною умовою створення точних і обчислювально ефективних електромагнітних моделей. Наведено практичні рекомендації щодо застосування методів машинного навчання в антенному проєктуванні.Ключові слова: штучна нейрона мережа, функція активації, адаптивний оптимізатор, мікросмужкова патч-антена, автоматичне проєктування антенСтаття надійшла до редакції 17.10.2025Radio phys. radio astron. 2026, 31(2): 098-107БІБЛІОГРАФІЧНИЙ СПИСОК1. James, J.R., Hall, P.S., Wood, C., 1981. Microstrip Antenna. Theory and Design, London: Peter Peregrinus LTD.2. Kumar, G., Kamala, P.R., 2003. Broadband Microstrip Antennas. Boston, London: Artech House.3. Waterhouse, R.B., 2013, Microstrip Patch Antennas: A Designer’s Guide. New York: Springer Science & Business Media.4. Patnaik, A., Anagnostou, D.E., Mishra, R.K., Christodoulou, C.G., Lyke, J.C., 2004. Applications of neural networks in wire- less communications, IEEE Antennas and Propagation Magazine, 46(3), pp. 130—137. DOI: 10.1109/MAP.2004.13741255. Feng, F., Zhang, J., Na, W., Zhang, W., Jin, J., Zhang, Q.-J., 2022. Artificial neural networks for microwave computer-aided design: The state of the art. IEEE Trans. Microw. Theory Tech., 70(11), pp. 4597—4619. DOI: 10.1109/TMTT.2022.31977516. Yu, Y., Zhang, Z., Cheng, Q.S., Liu, B., Wang, Y., Guo, C., Ye, T.T., 2022. State-of-the-art: AI-assisted surrogate mod- eling and optimization for microwave filters. IEEE Trans. Microw. Theory Tech., 70(11), pp. 4635—4651. DOI:10.1109/ TMTT.2022.32088987. Qi, S., Sarris, C.D., 2022. Deep neural networks for rapid simulation of planar microwave circuits based on their layouts.IEEE Trans. Microw. Theory Tech., 70(11), pp. 4805—4815. DOI: 10.1109/TMTT.2022.32102298. Swaminathan, M., Bhatti, O.W., Guo, Y., Huang, E., Akinwande, O., 2022. Bayesian Learning for Uncertainty Quanti-fication, Optimization, and Inverse Design. IEEE Trans. Microw. Theory Tech., 70(11), pp. 4620—4634. DOI: 10.1109/TMTT.2022.32064559. Ma, J., Dang, S., Li, P., Watkins, G., Morris, K.A., Beach, M.A., 2023. A learning-based methodology for microwave passive component design. IEEE Trans. Microw. Theory Tech., 71(7), pp. 3037—3050. DOI: 10.1109/TMTT.2023.323841810. Thakare, V.V., Singhal, P., 2010. Microstrip antenna design using artificial neural networks. Int. J. RF Microw. Comput.-Aid-ed Eng., 20(1), pp. 76—86. DOI: 10.1002/mmce.2041411. Rawat, A., Yadav, R.N., Shrivastava, S.C., 2012. Neural network applications in smart antenna arrays: A review. AEU — Int. J. Electron. Commun., 66(11), pp. 903—912. DOI: 10.1016/j.aeue.2012.03.01212. Khan, T., De, A., 2015. Modeling of microstrip antennas using neural networks techniques: a review. Int. J. RF Microw. Comput.-Aided Eng., 25(9), pp. 747—757. DOI: 10.1002/mmce.2091013. Shi, D., Lian, W., Cui, K., Leong, D.S.H., 2022. An intelligent antenna synthesis method based on machine learning. IEEE Trans. Antennas Propag., 70(7), pp. 4965—4976. DOI: 10.1109/TAP.2022.318269314. Massa, A., Salucci, M., 2022. 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Artificial neural networks for optimal design parameters prediction of microstrip patch antenna with wideband harmonic suppression. In: Proc. 2024 Second Int. Conf. on Microwave, Antenna and Communication(MAC). Dehradun, India, 04—06 Oct. 2024. DOI: 10.1109/MAC61551.2024.10837513 Видавничий дім «Академперіодика» 2026-06-16 Article Article http://rpra-journal.org.ua/index.php/ra/article/view/1494 РАДИОФИЗИКА И РАДИОАСТРОНОМИЯ; Vol 31, No 2 (2026); 98 RADIO PHYSICS AND RADIO ASTRONOMY; Vol 31, No 2 (2026); 98 РАДІОФІЗИКА І РАДІОАСТРОНОМІЯ; Vol 31, No 2 (2026); 98 2415-7007 1027-9636 en Copyright (c) 2026 RADIO PHYSICS AND RADIO ASTRONOMY |
| spellingShingle | artificial neural network (ANN) activation function adaptive optimizer microstrip patch antenna automatic antenna design Butov, S. Tuz, A. Morozova, O. Vinnichenko, S. Khardikov, O. Shulga, S. COMPUTATIONAL EFFICIENCY OF AN ARTIFICIAL NEURAL NETWORK IN OPTIMIZING THE OPERATING CONDITIONS OF PATCH ANTENNAS |
| title | COMPUTATIONAL EFFICIENCY OF AN ARTIFICIAL NEURAL NETWORK IN OPTIMIZING THE OPERATING CONDITIONS OF PATCH ANTENNAS |
| title_alt | ОБЧИСЛЮВАЛЬНА ЕФЕКТИВНІСТЬ ШТУЧНОЇ НЕЙРОННОЇ МЕРЕЖІ ДЛЯ ОПТИМІЗАЦІЇ УМОВ РОБОТИ МІКРОСМУЖКОВИХ АНТЕН |
| title_full | COMPUTATIONAL EFFICIENCY OF AN ARTIFICIAL NEURAL NETWORK IN OPTIMIZING THE OPERATING CONDITIONS OF PATCH ANTENNAS |
| title_fullStr | COMPUTATIONAL EFFICIENCY OF AN ARTIFICIAL NEURAL NETWORK IN OPTIMIZING THE OPERATING CONDITIONS OF PATCH ANTENNAS |
| title_full_unstemmed | COMPUTATIONAL EFFICIENCY OF AN ARTIFICIAL NEURAL NETWORK IN OPTIMIZING THE OPERATING CONDITIONS OF PATCH ANTENNAS |
| title_short | COMPUTATIONAL EFFICIENCY OF AN ARTIFICIAL NEURAL NETWORK IN OPTIMIZING THE OPERATING CONDITIONS OF PATCH ANTENNAS |
| title_sort | computational efficiency of an artificial neural network in optimizing the operating conditions of patch antennas |
| topic | artificial neural network (ANN) activation function adaptive optimizer microstrip patch antenna automatic antenna design |
| topic_facet | artificial neural network (ANN) activation function adaptive optimizer microstrip patch antenna automatic antenna design штучна нейрона мережа функція активації адаптивний оптимізатор мікросмужкова патч-антена автоматичне проєктування антен |
| url | http://rpra-journal.org.ua/index.php/ra/article/view/1494 |
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