Skip to main content
Springer Nature Link
Log in

1-D multi-channel CNN with transfer functions for inverse electromagnetic behaviors modeling and design optimization of high-dimensional filters

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

As an essential passive component in modern wireless communication systems, the design of high-frequency filters has become increasingly crucial. To achieve the target behavior specifications, traditional design methods are constrained by designers’ expertise or reliant on repetitive frequency sweeps using commercial software. Such processes suffer from low efficiency, limited applicability, and high computational costs. Artificial neural network-based modeling has become an important tool for designing devices. To realize accurate and fast electromagnetic modeling and design of passive components, this work proposes an inverse model integrating transfer functions and one-dimensional multi-channel convolutional neural networks (TF-1DMC-CNN). This model introduces transfer functions to ensure precise representation of electromagnetic responses while addressing the challenge of input dimensionality in wideband modeling. Input dimensions are reduced from 161 to 20 and 221 to 20 for two examples. The 1DMC-CNN processes distinct TF coefficients in each channel and extracts features in parallel. The geometrical parameters can be directly predicted in a single feedforward pass through the trained inverse model without needing iterative optimization. Compared to other inverse neural networks, the proposed model achieves the smallest testing errors. It obtains better model accuracy with fewer training samples, reducing data generation time. Compared to the traditional EM optimization method, this approach reduces CPU time for optimizations, enabling predictions of geometric structures that meet different design indexes. For multi-objective optimization, the proposed model predicts the structure within 0.16 seconds.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability and access

The datasets generated during the current study are available from the corresponding author on reasonable request.

References

  1. Song H-J (2021) Terahertz wireless communications: recent developments including a prototype system for short-range data downloading. IEEE Microwave Magazine 22(5):88–99

    Google Scholar 

  2. Krishna N, Padmasine K (2023) A review on microwave band pass filters: materials and design optimization techniques for wireless communication systems. Mater Sci Semicond Process 154:107181

    Google Scholar 

  3. Roy C, Wu K (2022) Homotopy optimization and ann modeling of millimeter-wave siw cruciform coupler. IEEE Transactions on Microwave Theory and Techniques 70(11):4751–4764

    Google Scholar 

  4. Na W, Yan S, Feng F, Liu W, Zhu L, Zhang Q-J (2021) Recent advances in knowledge-based model structure optimization and extrapolation techniques for microwave applications. Int J Numer Model Electron Netw, Devices and Fields 34(5):2879

    Google Scholar 

  5. Sagar MSI, Ouassal H, Omi AI, Wisniewska A, Jalajamony HM, Fernandez RE, Sekhar PK (2021) Application of machine learning in electromagnetics: mini-review. Electronics 10(22):2752

    Google Scholar 

  6. Yu Y, Zhang Z, Cheng QS, Liu B, Wang Y, Guo C, Ye TT (2022) State-of-the-art: AI-assisted surrogate modeling and optimization for microwave filters. IEEE Transactions on Microwave Theory and Techniques 70(11):4635–4651

    Google Scholar 

  7. Mejillones SC, Oldoni M, Moscato S, Macchiarella G (2020) Analytical synthesis of fully canonical cascaded-doublet prototype filters. IEEE Microwave and Wireless Components Letters 30(11):1017–1020

    Google Scholar 

  8. Koziel S, Bandler JW (2014) Rapid yield estimation and optimization of microwave structures exploiting feature-based statistical analysis. IEEE Transactions on Microwave Theory and Techniques 63(1):107–114

    Google Scholar 

  9. Koziel S, Pietrenko-Dabrowska A (2022) Expedited variable-resolution surrogate modeling of miniaturized microwave passives in confined domains. IEEE Transactions on Microwave Theory and Techniques 70(11):4740–4750

    Google Scholar 

  10. Jansson E, Thiringer T, Grunditz E (2020) Convergence of core losses in a permanent magnet machine, as function of mesh density distribution, a case-study using finite-element analysis. IEEE Transactions on Energy Conversion 35(3):1667–1675

    Google Scholar 

  11. Yang S-H, Liu X-B, Tan T-J, Zhang L, Su C, Zhou H-F, Xie X-L (2023) Realization of superhuman intelligence in microstrip filter design based on clustering-reinforcement learning. Appl Intell pp 1–14

  12. Liu Y-F, Peng L, Shao W (2022) An efficient knowledge-based artificial neural network for the design of circularly polarized 3-d-printed lens antenna. IEEE Transactions on Antennas and Propagation 70(7):5007–5014

    Google Scholar 

  13. Salehi MR, Noori L, Abiri E (2016) Prediction of matching condition for a microstrip subsystem using artificial neural network and adaptive neuro-fuzzy inference system. Int J Electron 103(11):1882–1893

    Google Scholar 

  14. Feng F, Zhang C, Ma J, Zhang Q-J (2015) Parametric modeling of em behavior of microwave components using combined neural networks and pole-residue-based transfer functions. IEEE Transactions on Microwave Theory and Techniques 64(1):60–77

    Google Scholar 

  15. Feng F, Na W, Jin J, Zhang J, Zhang W, Zhang Q-J (2022) Artificial neural networks for microwave computer-aided design: the state of the art. IEEE Transactions on Microwave Theory and Techniques

  16. Feng F, Na W, Jin J, Zhang W, Zhang Q-J (2021) Anns for fast parameterized em modeling: the state of the art in machine learning for design automation of passive microwave structures. IEEE Microwave Magazine 22(10):37–50

    Google Scholar 

  17. Zhang Q-J, Gupta KC, Devabhaktuni VK (2003) Artificial neural networks for rf and microwave design-from theory to practice. IEEE Transactions on Microwave Theory and Techniques 51(4):1339–1350

    Google Scholar 

  18. Xiao L-Y, Shao W, Jin F-L, Wang B-Z, Joines WT, Liu QH (2019) Semisupervised radial basis function neural network with an effective sampling strategy. IEEE Transactions on Microwave Theory and Techniques 68(4):1260–1269

    Google Scholar 

  19. Yahya SI, Rezaei A, Nouri L (2021) The use of artificial neural network to design and fabricate one of the most compact microstrip diplexers for broadband l-band and s-band wireless applications. Wirel Netw 27:663–676

    Google Scholar 

  20. Wang R, Su J, Xie W, Lin Z (2023) Knowledge-based neural network with bayesian optimization for efficient nonlinear rf device modeling. Int J Numer Model Electron Netw, Dev & Fields, 3157

  21. Na W, Feng F, Zhang C, Zhang Q-J (2016) A unified automated parametric modeling algorithm using knowledge-based neural network and \(l_1\) optimization. IEEE Transactions on Microwave Theory and Techniques 65(3):729–745

    Google Scholar 

  22. Xiao L-Y, Shao W, Jin F-L, Wang B-Z, Liu QH (2021) Inverse artificial neural network for multiobjective antenna design. IEEE Transactions on Antennas and Propagation 69(10):6651–6659

    Google Scholar 

  23. Pietrenko-Dabrowska A, Koziel S (2020) Accelerated multiobjective design of miniaturized microwave components by means of nested kriging surrogates. Int J RF Microw Comput-Aided Eng 30(4):22124

    Google Scholar 

  24. Kapetanakis TN, Vardiambasis IO, Ioannidou MP, Maras A (2018) Neural network modeling for the solution of the inverse loop antenna radiation problem. IEEE Transactions on Antennas and Propagation 66(11):6283–6290

    Google Scholar 

  25. Pan G, Wu Y, Yu M, Fu L, Li H (2020) Inverse modeling for filters using a regularized deep neural network approach. IEEE Microwave and Wireless Components Letters 30(5):457–460

    Google Scholar 

  26. Zhang C, Jin J, Na W, Zhang Q-J, Yu M (2018) Multivalued neural network inverse modeling and applications to microwave filters. IEEE Transactions on Microwave Theory and Techniques 66(8):3781–3797

    Google Scholar 

  27. Sedaghat M, Trinchero R, Firouzeh ZH, Canavero FG (2022) Compressed machine learning-based inverse model for design optimization of microwave components. IEEE Transactions on Microwave Theory and Techniques 70(7):3415–3427

    Google Scholar 

  28. Jin J, Zhang C, Feng F, Na W, Ma J, Zhang Q-J (2019) Deep neural network technique for high-dimensional microwave modeling and applications to parameter extraction of microwave filters. IEEE Transactions on Microwave Theory and Techniques 67(10):4140–4155

    Google Scholar 

  29. Wu Y, Pan G, Lu D, Yu M (2022) Artificial neural network for dimensionality reduction and its application to microwave filters inverse modeling. IEEE Transactions on Microwave Theory and Techniques 70(11):4683–4693

    Google Scholar 

  30. Cao Y, Wang G, Zhang Q-J (2009) A new training approach for parametric modeling of microwave passive components using combined neural networks and transfer functions. IEEE Transactions on Microwave Theory and Techniques 57(11):2727–2742

    Google Scholar 

  31. Feng F, Zhang C, Zhang S, Zhang Q-J, et al (2016) Parallel em optimization approach to microwave filter design using feature assisted neuro-transfer functions. In: 2016 IEEE MTT-S International microwave symposium (IMS), IEEE, pp 1–3

  32. Zhang J, Feng F, Zhang W, Jin J, Ma J, Zhang Q-J (2020) A novel training approach for parametric modeling of microwave passive components using padé via lanczos and em sensitivities. IEEE Transactions on Microwave Theory and Techniques 68(6):2215–2233

    Google Scholar 

  33. Zhang J, Chen J, Guo Q, Liu W, Feng F, Zhang Q-J (2022) Parameterized modeling incorporating mor-based rational transfer functions with neural networks for microwave components. IEEE Microwave and Wireless Components Letters 32(5):379–382

    Google Scholar 

  34. Zhuo Y, Feng F, Zhang J, Zhang Q-J (2022) Parametric modeling incorporating joint polynomial-transfer function with neural networks for microwave filters. IEEE Transactions on Microwave Theory and Techniques 70(11):4652–4665

  35. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Google Scholar 

  36. Jin J, Feng F, Zhang J, Yan S, Na W, Zhang Q (2020) A novel deep neural network topology for parametric modeling of passive microwave components. IEEE Access 8:82273–82285

    Google Scholar 

  37. Zhou Y, Xie J, Ren Q, Zhang HH, Liu QH (2022) Fast multi-physics simulation of microwave filters via deep hybrid neural network. IEEE Transactions on Antennas and Propagation 70(7):5165–5178

    Google Scholar 

  38. Hongyo R, Egashira Y, Hone TM, Yamaguchi K (2019) Deep neural network-based digital predistorter for doherty power amplifiers. IEEE Microwave and Wireless Components Letters 29(2):146–148

    Google Scholar 

  39. Zhang S, Hu X, Liu Z, Sun L, Han K, Wang W, Ghannouchi FM (2021) Deep neural network behavioral modeling based on transfer learning for broadband wireless power amplifier. IEEE Microwave and Wireless Components Letters 31(7):917–920

    Google Scholar 

  40. Bayoudh K, Hamdaoui F, Mtibaa A (2021) Transfer learning based hybrid 2d–3d cnn for traffic sign recognition and semantic road detection applied in advanced driver assistance systems. Appl Intell 51:124–142

    Google Scholar 

  41. Lee H, Kwon H (2017) Going deeper with contextual cnn for hyperspectral image classification. IEEE Transactions on Image Processing 26(10):4843–4855

    MathSciNet  Google Scholar 

  42. Soni S, Chouhan SS, Rathore SS (2023) Textconvonet: a convolutional neural network based architecture for text classification. Appl Intell 53(11):14249–14268

    Google Scholar 

  43. Ma K, Tang C, Zhang W, Cui B, Ji K, Chen Z, Abraham A (2023) Dc-cnn: dual-channel convolutional neural networks with attention-pooling for fake news detection. Appl Intell 53(7):8354–8369

    Google Scholar 

  44. Acharya UR, Fujita H, Oh SL, Hagiwara Y, Tan JH, Adam M, Tan RS (2019) Deep convolutional neural network for the automated diagnosis of congestive heart failure using ecg signals. Appl Intell 49:16–27

    Google Scholar 

  45. Li L, Wang LG, Teixeira FL, Liu C, Nehorai A, Cui TJ (2018) Deepnis: deep neural network for nonlinear electromagnetic inverse scattering. IEEE Transactions on Antennas and Propagation 67(3):1819–1825

    Google Scholar 

  46. Luo H-Y, Shao W, Ding X, Wang B-Z, Cheng X (2022) Shape modeling of microstrip filters based on convolutional neural network. IEEE Microwave and Wireless Components Letters 32(9):1019–1022

    Google Scholar 

  47. Desai M, Ghosh P, Kumar A, Chaudhury B (2022) Deep-learning architecture-based approach for 2-d-simulation of microwave plasma interaction. IEEE Transactions on Microwave Theory and Techniques 70(12):5359–5368

    Google Scholar 

  48. Ma J, Dang S, Li P, Watkins G, Morris K, Beach M (2022) Transfer learning for the behavior prediction of microwave structures. IEEE Microwave and Wireless Technology Letters 33(2):126–129

    Google Scholar 

  49. Ma J, Dang S, Li P, Watkins GT, Morris K, Beach M (2023) A learning-based methodology for microwave passive component design. IEEE Transactions on Microwave Theory and Techniques

  50. Zhang S, Wei H-L, Ding J (2023) An effective zero-shot learning approach for intelligent fault detection using 1d cnn. Appl Intell 53(12):16041–16058

    Google Scholar 

  51. Kiranyaz S, Ince T, Gabbouj M (2015) Real-time patient-specific ecg classification by 1-d convolutional neural networks. IEEE Transactions on Biomedical Engineering 63(3):664–675

    Google Scholar 

  52. Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman DJ (2021) 1d convolutional neural networks and applications: a survey. Mech Syst Signal Process 151:107398

    Google Scholar 

  53. Yin Y, Wang S, Zhou J (2023) Multisensor-based tool wear diagnosis using 1d-cnn and dgcca. Appl Intell 53(4):4448–4461

    Google Scholar 

  54. Yu C, Li Q, Feng F, Zhang Q-J (2022) Convolutional neural network with adaptive batch-size training technique for high-dimensional inverse modeling of microwave filters. IEEE Microwave and Wireless Technology Letters 33(2):122–125

    Google Scholar 

  55. Gustavsen B, Semlyen A (1999) Rational approximation of frequency domain responses by vector fitting. IEEE Transactions on Power Delivery 14(3):1052–1061

    Google Scholar 

  56. Szegedy C, Liu W, Jia YQ, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR). IEEE Conference on computer vision and pattern recognition, pp 1–9, Boston, MA, USA

  57. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Proceedings 32nd International Conference Machine Learning, vol 37. Lille, France, pp 448–456

  58. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  Google Scholar 

  59. Schmidt SR, Launsby RG (1989) Understanding industrial designed experiments. Air Academy Press, USA

    Google Scholar 

  60. Zhao Z, Feng F, Zhang W, Zhang J, Jin J, Zhang Q-J (2020) Parametric modeling of em behavior of microwave components using combined neural networks and hybrid-based transfer functions. IEEE Access 8:93922–93938

    Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China under Grant 61927804.

Author information

Authors and Affiliations

  1. Department of Automation, Tsinghua University, Haidian District, Beijing, 100084, Beijing, China

    Yimin Ren, Xiaojiao Deng, Zhengyang You & Xiaoping Zheng

Authors
  1. Yimin Ren
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xiaojiao Deng
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zhengyang You
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xiaoping Zheng
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Conceptualization: Yimin Ren, Xiaoping Zheng; Methodology: Yimin Ren, Zhengyang You; Formal analysis and investigation: Yimin Ren, Zhengyang You; Writing - original draft preparation: Yimin Ren; Writing - review and editing: Xiaojiao Deng, Xiaoping Zheng; Funding acquisition: Xiaoping Zheng; Supervision: Xiaoping Zheng. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xiaoping Zheng.

Ethics declarations

Informed consent

This paper does not use any third-party dataset. The datasets generated in this study does not involve research on humans or animals. The datasets generated in this study meets the requirements for accessibility.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ren, Y., Deng, X., You, Z. et al. 1-D multi-channel CNN with transfer functions for inverse electromagnetic behaviors modeling and design optimization of high-dimensional filters. Appl Intell 54, 503–521 (2024). https://doi.org/10.1007/s10489-023-05200-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-05200-4

Keywords