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Antenna optimization based on master-apprentice broad learning system

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Abstract

In order to improve the efficiency of antenna optimization design, a surrogate model is often used to replace the full-wave electromagnetic simulation software. Broad learning system (BLS) provides an alternative method for deep structure, aiming to overcome the drawback of excessive time-consuming training process, however, usually not with satisfactory accuracy. In order to further improve the performance of the model, master-apprentice (MA) behavior is proposed in this paper, using the current BLS training results as the priori knowledge, which are taken as fixed features to the next BLS hidden layer for further training. Each MA behavior forms a double BLS structure, which is composed of two parts, the models trained before and after are called master BLS (MBLS) and apprentice BLS (ABLS) respectively. These two subsystems together constitute a master-apprentice BLS (MABLS). Two antenna examples, rectangular microstrip antenna (RMSA) and WLAN dual-band monopole antenna (DBMA), and 10 UCI regression datasets are employed to demonstrate the effectiveness of the proposed model.

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References

  1. Fan XH, Tian YB, Zhao Y (2018) Optimal design of microwave devices by fitness-estimation-based particle swarm optimization algorithm. Appl Comput Electromagn Soc J 33(11):1259–1267

    Google Scholar 

  2. Grout V, Akinsolu MO, Liu B, Lazaridis PI, Mistry KK, Zaharis ZD (2019) Software solutions for antenna design exploration: a comparison of packages, tools, techniques, and algorithms for various design challenges. IEEE Antennas Propag Mag 61(3):48–59. https://doi.org/10.1109/MAP.2019.2907887

    Article  Google Scholar 

  3. Jacobs JP (2015) Efficient Resonant frequency modeling for dual-band microstrip antennas by Gaussian process regression. IEEE Antennas Wirel Propag Lett 14:337–341. https://doi.org/10.1109/LAWP.2014.2362937

    Article  Google Scholar 

  4. Jais MI et al (2019) A Fuzzy-based angle-of-arrival estimation system (AES) using radiation pattern reconfigurable (RPR) antenna and modified Gaussian membership function. IEEE Access 7:145477–145488. https://doi.org/10.1109/ACCESS.2019.2945789

    Article  Google Scholar 

  5. Han SD, Tian YB, Ding WT, Li PF (2021) Resonant frequency modeling of microstrip antenna based on deep kernel learning. IEEE Access 9:39067–39076. https://doi.org/10.1109/ACCESS.2021.3062940

    Article  Google Scholar 

  6. Chen Y et al (2017) Optimization of reflection coefficient of microstrip antennas based on KBNN exploiting GPR model. IET Microw Antennas Propag 12(4):602–606

    Article  Google Scholar 

  7. Xiao LY, Shao W, Ding X, Liu QH, Joines WT (2019) Multigrade artificial neural network for the design of finite periodic arrays. IEEE Trans Antennas Propag 67(5):3109–3116

    Article  Google Scholar 

  8. Xiao L, Shao W, Jin F, Wang B (2018) Multiparameter modeling with ANN for antenna design. IEEE Trans Antennas Propag 66(7):3718–3723. https://doi.org/10.1109/TAP.2018.2823775

    Article  Google Scholar 

  9. Tarkowski M, Kulas L (2019) RSS-based DoA estimation for espar antennas using support vector machine. IEEE Antennas Wirel Propag Lett 18(4):561–565. https://doi.org/10.1109/LAWP.2019.2891021

    Article  Google Scholar 

  10. Sun FY, Tian YB, Ren ZL (2016) Modeling the resonant frequency of compact microstrip antenna by the PSO-based SVM with the hybrid kernel function. Int J Numer Model Electron Netw Dev Fields 29(6):1129–1139. https://doi.org/10.1002/jnm.2171

    Article  Google Scholar 

  11. Gao Y, Li YB (2017) Misfire identification of automobile engines based on wavelet packet and extreme learning machine. J Meas Sci Instrum 8(4):384–395

    Google Scholar 

  12. Zhang H, Ai B, Xu W, Xu L, Cui S (2019) Multi-antenna channel interpolation via tucker decomposed extreme learning machine. IEEE Trans Veh Technol 68(7):7160–7163. https://doi.org/10.1109/TVT.2019.2913865

    Article  Google Scholar 

  13. Xiao LY, Shao W, Ding X, Wang BZ (2018) Dynamic adjustment kernel extreme learning machine for microwave component design. IEEE Trans Microw Theory Techn 66(10):4452–4461

    Article  Google Scholar 

  14. Chen CLP, Liu Z (2018) Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans Neural Netw Learn Syst 29(1):10–24. https://doi.org/10.1109/TNNLS.2017.2716952

    Article  MathSciNet  Google Scholar 

  15. Chen CLP, Liu Z, Feng S (2019) Universal approximation capability of broad learning system and its structural variations. IEEE Trans Neural Netw Learn Syst 30(4):1191–1204. https://doi.org/10.1109/TNNLS.2018.2866622

    Article  MathSciNet  Google Scholar 

  16. Chen CLP, Wan JZ (1999) A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction. IEEE Trans Syst Man Cybern Part B (Cybernetics) 29(1):62–72. https://doi.org/10.1109/3477.740166

    Article  Google Scholar 

  17. Pao YH, Takefuji Y (1992) Functional-link net computing: theory, system architecture, and functionalities. Computer 25(5):76–79

    Article  Google Scholar 

  18. Igelnik B, Pao YH (1995) Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans Neural Netw 6(6):1320–1329. https://doi.org/10.1109/72.471375

    Article  Google Scholar 

  19. Pao YH, Park GH, Sobajic DJ (1994) Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2):163–180

    Article  Google Scholar 

  20. Le CY, Boser B, Denker J et al (1989) Handwritten digit recognition with a back-propagation network. Adv Neural Inf Process Syst 2:396–404

    Google Scholar 

  21. Hoerl AE, Kennard RW (1970) Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12(1):55–67

    Article  Google Scholar 

  22. Gao S, Guo G, Huang H, Cheng X, Chen CLP (2020) An end-to-end broad learning system for event-based object classification. IEEE Access 8:45974–45984. https://doi.org/10.1109/ACCESS.2020.2978109

    Article  Google Scholar 

  23. Zhang T, Chen R, Yang X, Guo S (2019) Rich feature combination for cost-based broad learning system. IEEE Access 7:160–172. https://doi.org/10.1109/ACCESS.2018.2885164

    Article  Google Scholar 

  24. Chu F, Liang T, Chen CLP, Wang X, Ma X (2020) Weighted broad learning system and its application in nonlinear industrial process modeling. IEEE Trans Neural Netw Learn Syst 31(8):3017–3031. https://doi.org/10.1109/TNNLS.2019.2935033

    Article  MathSciNet  Google Scholar 

  25. Xu M, Han M, Chen CLP, Qiu T (2020) Recurrent broad learning systems for time series prediction. IEEE Trans Cybern 50(4):1405–1417. https://doi.org/10.1109/TCYB.2018.2863020

    Article  Google Scholar 

  26. Han M, Feng S, Chen CLP, Xu M, Qiu T (2019) Structured manifold broad learning system: a manifold perspective for large-scale chaotic time series analysis and prediction. IEEE Trans Knowl Data Eng 31(9):1809–1821. https://doi.org/10.1109/TKDE.2018.2866149

    Article  Google Scholar 

  27. Badirli S, Liu X, Xing Z et al (2020) Gradient boosting neural networks: Grownet. arXiv preprint 2002.07971, 2020.

  28. Kara M (1996) Closed-form expressions for the resonant frequency of rectangular microstrip antenna elements with thick substrates. Microw Opt Technol Lett 12(3):131–136

    Article  Google Scholar 

  29. Kara M (1996) The resonant frequency of rectangular microstrip antenna elements with various substrate thicknesses. Microw Opt Technol Lett 11(2):55–59

    Article  Google Scholar 

  30. Guney K, Sagiroglu S, Erler M (2002) Generalized neural method to determine resonant frequencies of various microstrip antennas. Int J RF Microwave Comput Aided Eng 12(1):131–139

    Article  Google Scholar 

  31. Sagiroglu S, Kalinli A (2005) Determining resonant frequencies of various microstrip antennas within a single neural model trained using parallel tabu search algorithm. Electromagnetics 25(6):551–565

    Article  Google Scholar 

  32. Tian YB, Zhang SL, Li JY (2011) Modeling resonant frequency of microstrip antenna based on neural network ensemble. Int J Numer Model Electron Netw Dev Fields 24(1):78–88

    Article  Google Scholar 

  33. Zhang GM, Hong JS, Wang BZ et al (2007) A novel planar monopole antenna with an H-shaped ground plane for dual-band WLAN applications. J Electromagn Waves Appl 21(15):2229–2239

    Article  Google Scholar 

  34. Zhang TL, Yan ZH, Song Y et al (2009) Compact CPW-fed planar monopole antenna for dual-band WLAN applications. Microw Opt Technol Lett 51(5):1377–1379

    Article  Google Scholar 

  35. Tian WC, Wu DW, Chao Q (2018) Application of genetic algorithm in M-N reconfigurable antenna array based on RF MEMS switches. Mod Phys Lett B 32(30):1850365

    Article  Google Scholar 

  36. Blake CL and Merz CJ (1998) UCI repository of machine learning databases Dept. Inf. Comput. Sci.,Univ. California, Irvine, Irvine, CA, USA. [Online]. http://archive.ics.uci.edu/ml/datasets.php. Accessed 25 June 2021

  37. Ding WT, Tian YB, Han SD, Yuan HN (2021) Greedy broad learning system. IEEE Access 9:79307–79315. https://doi.org/10.1109/ACCESS.2021.3084610

    Article  Google Scholar 

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Funding

This work is supported by the National Natural Science Foundation of China (NSFC) under No. 61771225, and the Qinglan Project of Jiangsu Higher Education.

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Author notes

  1. Weitong Ding and Yubo Tian are co-first authors.

Authors and Affiliations

  1. School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu, China

    Weitong Ding, Pengfei Li, Huining Yuan & Rui Li

  2. School of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou, 510725, Guangdong, China

    Yubo Tian

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  1. Weitong Ding
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  2. Yubo Tian
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  3. Pengfei Li
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  4. Huining Yuan
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  5. Rui Li
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Correspondence to Yubo Tian.

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Ding, W., Tian, Y., Li, P. et al. Antenna optimization based on master-apprentice broad learning system. Int. J. Mach. Learn. & Cyber. 13, 461–470 (2022). https://doi.org/10.1007/s13042-021-01418-1

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  • DOI: https://doi.org/10.1007/s13042-021-01418-1

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