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Application of Machine Learning for Estimating Empirical Parameters for Rectangular Microstrip Patch Antenna

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Proceedings of the International e-Conference on Intelligent Systems and Signal Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1370))

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Abstract

An antenna is a rudimentary element in communication technology. Combination with trending technology is apparent with a certain zenith for advancement in the domain. An approach of predicting the resonant frequency, return loss and input impedance by taking hundreds of combinations of length and width of a rectangular radiating patch is made by considering rectangular microstrip patch antenna. The design of this is made by using Ansys high-frequency structure simulator (HFSS) software. Design and optimization are made on 2.4 GHz patch antenna. Optimization of parameters is done by extracting the dataset from simulated design and optimization. Ridge regression, linear regression and decision tree regressor algorithms are used for training the model; moreover, the model works more efficiently with decision tree regressor. However, the model works very well for the decision tree by predicting practical resonant frequency, input impedance and return loss with 99.66%, 99.98% and 80.04% accuracy, respectively, on unseen data.

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References

  1. D. Ustun, A. Toktas, A. Akdagli, Deep neural network–based soft computing the resonant frequency of E–shaped patch antennas. Int. J. Electron. Commun. https://doi.org/10.1016/J.AEUE.2019.02.011

  2. T. Padmavathy, D. Bhargava, P. Venkatesh, N. Sivakumar, Design and development of microstrip patch antenna with circular and rectangular slot for structural health monitoring. Pers. Ubiquitous Comput. Springer (2018). https://doi.org/10.1007/s00779-018-1130-9

  3. A. Kayabasi, A. Toktas, E. Yigit, K. Sabanci, Triangular quad-port multi-polarized UWB MIMO antenna with enhanced isolation using neutralization ring. Int. J. Electron. Commun. 85, 47–53 (2018). https://doi.org/10.1016/J.AEUE.2017.12.027

  4. D. Ustun, A. Akdagli, Design of band–notched UWB antenna using a hybrid optimization based on ABC and DE algorithms. Int. J. Electron. Commun. 87, 10–21 (2018). https://doi.org/10.1016/J.AEUE.2018.02.001

  5. Y. Sharma, H.H. Zhang, H. Xin, Machine learning techniques for optimizing design of double T-shaped monopole antenna. IEEE Trans. Antennas Propag. (2019). https://doi.org/10.1109/TAP.2020.2966051

  6. A. Kayabasi, MLP and KNN algorithm model applications for determining the operating frequency of A-shaped patch antennas. Int. J. Intell. Syst. Appl. Eng. Adv. Technol. Sci. (2017). ISSN: 2147-6799. https://doi.org/10.18201/ijisae.2017531432

  7. A. Ali, A. Toktas, A. Kayabasi, I. Develi, An application of ANN to compute resonant frequency of E-shaped compact microstrip antennas. J. Electr. Eng. 64(5), 317–322 (2013). https://doi.org/10.2478/jee-2013-0046

    Article  Google Scholar 

  8. S. Gangwar, R. Gangwar, B. Kanaujia, Paras, Resonant frequency on circular microstrip antenna using artificial neural networks. Indian J. Radio Space Phys. 37, 204–208 (2008)

    Google Scholar 

  9. S. Jain, A. Patnaik, S. Sinha, Design of custom-made stacked patch antennas: a machine learning approach. Int. J. Mach. Learn. Cybersecur. 4, 189–194 (2013). https://doi.org/10.1007/s13042-012-0084-x

    Article  Google Scholar 

  10. S. Jain, Bandwidth enhancement of patch antennas using neural network dependent modified optimizer. Int. J. Microw. Wirel. Technol. (2015). https://doi.org/10.1017/S1759078715000616

  11. M. Raya, S. Pal, K. Ali, Design of inset fed rectangular shaped microstrip patch antenna using deep neural networks, in International Conference on Computer and Information Technology (2019)

    Google Scholar 

  12. A. Gehani, D. Pujara, D. Adhyaru, Analysis of an elliptical patch antenna using artificial neural networks, in IEEE Asia-Pacific Conference on Antennas and Propagation (2012)

    Google Scholar 

  13. C.A. Balanis, Antenna Theory—Analysis and Design

    Google Scholar 

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Author information

Authors and Affiliations

  1. V. T. Patel Department of Electronics and Communication Engineering, Chandubhai S. Patel Institute of Science and Technology, Charotar University of Science and Technology (CHARUSAT), Anand, Gujarat, India

    Pankti Shah & Amit Patel

Authors
  1. Pankti Shah
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  2. Amit Patel
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Editor information

Editors and Affiliations

  1. Department of Electronics and Communication, G H Patel College of Engineering and Technology, Vallabh Vidyanagar, Anand, Gujarat, India

    Falgun Thakkar

  2. Department of Electronics and Communication, G H Patel College of Engineering and Technology, Vallabh Vidyanagar, Anand, Gujarat, India

    Geetali Saha

  3. Department of Electrical and Electronics Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

    Celia Shahnaz

  4. Department of Computer Science and Information Management, Providence University, Taichung, Taiwan

    Yu-Chen Hu

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Shah, P., Patel, A. (2022). Application of Machine Learning for Estimating Empirical Parameters for Rectangular Microstrip Patch Antenna. In: Thakkar, F., Saha, G., Shahnaz, C., Hu, YC. (eds) Proceedings of the International e-Conference on Intelligent Systems and Signal Processing. Advances in Intelligent Systems and Computing, vol 1370. Springer, Singapore. https://doi.org/10.1007/978-981-16-2123-9_52

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