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Zhou: \u201cGwo-ga-xgboost-based model for radio-frequency power amplifier under different temperatures,\u201d Expert Systems with Applications <b>278<\/b> (2025) 127439 (DOI: 10.1016\/j.eswa.2025.127439).","DOI":"10.1016\/j.eswa.2025.127439"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] J. Jha, <i>et al<\/i>.: \u201cA mixer architecture using GaN-based split-gate nanowire transistor,\u201d Nanotechnology <b>35<\/b> (2024) 415202 (DOI: 10.1088\/1361-6528\/ad63b0).","DOI":"10.1088\/1361-6528\/ad63b0"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] A. Merad and M. Merad: \u201cThe Dunkl-Duffin-Kemmer-Petiau oscillator,\u201d Few-Body Systems <b>62<\/b> (2021) 98 (DOI: 10.1007\/s00601-022-01753-1).","DOI":"10.1007\/s00601-021-01683-4"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] D. Denis, <i>et al<\/i>.: \u201cCoupled electrothermal, electromagnetic, and physical modeling of microwave power FETs,\u201d IEEE Trans. Microw. Theory Techn. <b>54<\/b> (2006) 2465 (DOI: 10.1109\/TMTT.2006.875797).","DOI":"10.1109\/TMTT.2006.875797"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] S. Sharma and V. Kumar: \u201cLook-up table based IV model for GaN HEMT devices for Microwave Applications,\u201d Microprocessors and Microsystems <b>83<\/b> (2021) 103952 (DOI: 10.1016\/j.micpro.2021.103952).","DOI":"10.1016\/j.micpro.2021.103952"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] J. Wood and D.E. Root: \u201cBias-dependent linear scalable millimeter-wave FET model,\u201d IEEE Trans. Microw. Theory Techn. <b>48<\/b> (2002) 2352 (DOI: 10.1109\/22.898984).","DOI":"10.1109\/22.898984"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] G. Crupi, <i>et al<\/i>.: \u201cDetermination and validation of new nonlinear FinFET model based on lookup tables,\u201d IEEE Microw. Wireless Compon. Lett. <b>17<\/b> (2007) 361 (DOI: 10.1109\/LMWC.2007.895711).","DOI":"10.1109\/LMWC.2007.895711"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] F. Feng, <i>et al<\/i>.: \u201cArtificial neural networks for microwave computer-aided design: The state of the art,\u201d IEEE Trans. Microw. Theory Techn. <b>70<\/b> (2022) 4597 (DOI: 10.1109\/TMTT.2022.3197751).","DOI":"10.1109\/TMTT.2022.3197751"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] F. Feng, <i>et al<\/i>.: \u201cANNs for fast parameterized EM modeling: The state of the art in machine learning for design automation of passive microwave structures,\u201d IEEE Microw. Mag. <b>22<\/b> (2021) 37 (DOI: 10.1109\/MMM.2021.3095990).","DOI":"10.1109\/MMM.2021.3095990"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] X. Wang, <i>et al<\/i>.: \u201cAnalytical separated neuro-space mapping modeling method of power transistor,\u201d Micromachines <b>14<\/b> (2023) 426 (DOI: 10.3390\/mi14020426).","DOI":"10.3390\/mi14020426"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] P. Kulkarni: \u201cApplications of artificial intelligence\/machine learning in RF, microwaves, and signal processing,\u201d IEEE Microw. Mag. <b>22<\/b> (2021) 41 (DOI: 10.1109\/mmm.2021.3109545).","DOI":"10.1109\/MMM.2021.3109545"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] O. Ayoub, <i>et al<\/i>.: \u201cExplainable artificial intelligence in communication networks: A use case for failure identification in microwave networks,\u201d Computer Networks <b>219<\/b> (2022) 109466 (DOI: 10.1016\/j.comnet.2022.109466).","DOI":"10.1016\/j.comnet.2022.109466"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] E. Mousa Ali, <i>et al<\/i>.: \u201cA novel rectifying circuit for microwave power harvesting system,\u201d International Journal of RF and Microwave Computer-Aided Engineering <b>27<\/b> (2017) e21083 (DOI: 10.1002\/mmce.21083).","DOI":"10.1002\/mmce.21083"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] M. Jamshidi, <i>et al<\/i>.: \u201cA fast surrogate model-based algorithm using multilayer perceptron neural networks for microwave circuit design,\u201d Algorithms <b>16<\/b> (2023) 324 (DOI: 10.3390\/a16070324).","DOI":"10.3390\/a16070324"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] W. Na, <i>et al<\/i>.: \u201cAutomated multilayer neural network structure adaptation method with <i>l<\/i><sub>1<\/sub> regularization for microwave modeling,\u201d IEEE Microw. Wireless Compon. Lett. <b>32<\/b> (2022) 815 (DOI: 10.1109\/LMWC.2022.3153058).","DOI":"10.1109\/LMWC.2022.3153058"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] P. Dey, <i>et al<\/i>.: \u201cRegularizing multilayer perceptron for robustness,\u201d IEEE Trans. Syst., Man, Cybern. Syst. <b>48<\/b> (2017) 1255 (DOI: 10.1109\/TSMC.2017.2664143).","DOI":"10.1109\/TSMC.2017.2664143"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] J. Deng, <i>et al<\/i>.: \u201cANN-assisted SPICE model solutions,\u201d International Journal of Numerical Modelling: Electronic Networks, Devices and Fields <b>38<\/b> (2025) e70065 (DOI: 10.1002\/jnm.70065).","DOI":"10.1002\/jnm.70065"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] R.P. Martinez, <i>et al<\/i>.: \u201cAssessment and comparison of measurement-based large-signal FET models for GaN HEMTs,\u201d IEEE Trans. Microw. Theory Techn. <b>72<\/b> (2024) 2692 (DOI: 10.1109\/tmtt.2023.3349172).","DOI":"10.1109\/TMTT.2023.3349172"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] W. Na, <i>et al<\/i>.: \u201cAutomated neural-network-based model generation algorithms for microwave applications,\u201d IEEE Microw. Mag. <b>26<\/b> (2025) 18 (DOI: 10.1109\/MMM.2024.3486596).","DOI":"10.1109\/MMM.2024.3486596"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] W. Na, <i>et al<\/i>.: \u201cAutomated neural network-based multiphysics parametric modeling of microwave components,\u201d IEEE Access <b>7<\/b> (2019) 141153 (DOI: 10.1109\/ACCESS.2019.2944162).","DOI":"10.1109\/ACCESS.2019.2944162"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] W. Tai, <i>et al<\/i>.: \u201cExponential passive filter design for switched neural networks with time-delay and reaction-diffusion terms,\u201d Modern Physics Letters B <b>35<\/b> (2021) 2150434 (DOI: 10.1142\/S0217984921504340).","DOI":"10.1142\/S0217984921504340"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] K.C. Sahu, <i>et al<\/i>.: \u201cSurrogate modeling of passive microwave circuits using recurrent neural networks and domain confinement,\u201d Scientific Reports <b>15<\/b> (2025) 13322 (DOI: 10.1038\/s41598-025-91643-3).","DOI":"10.1038\/s41598-025-91643-3"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] W. Liu, <i>et al<\/i>.: \u201cA Wiener-type dynamic neural network approach to the modeling of nonlinear microwave devices,\u201d IEEE Trans. Microw. Theory Techn. <b>65<\/b> (2017) 2043 (DOI: 10.1109\/TMTT.2017.2657501).","DOI":"10.1109\/TMTT.2017.2657501"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] L. Zhu, <i>et al<\/i>.: \u201cA novel dynamic neuro-space mapping approach for nonlinear microwave device modeling,\u201d IEEE Microw. Wireless Compon. Lett. <b>26<\/b> (2016) 131 (DOI: 10.1109\/lmwc.2016.2516761).","DOI":"10.1109\/LMWC.2016.2516761"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] W. Liu, <i>et al<\/i>.: \u201cA time delay neural network based technique for nonlinear microwave device modeling,\u201d Micromachines <b>11<\/b> (2020) 831 (DOI: 10.3390\/mi11090831).","DOI":"10.3390\/mi11090831"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] Z. Zuo, <i>et al<\/i>.: \u201cAnalysis and improvement of self-heating effect based on GaN HEMT devices,\u201d Materials Research Express <b>9<\/b> (2022) 075903 (DOI: 10.1088\/2053-1591\/ac82a8).","DOI":"10.1088\/2053-1591\/ac82a8"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] K.S. Yuk, <i>et al<\/i>.: \u201cA wideband multiharmonic empirical large-signal model for high-power GaN HEMTs with self-heating and charge-trapping effects,\u201d IEEE Trans. Microw. Theory Techn. <b>57<\/b> (2009) 3322 (DOI: 10.1109\/TMTT.2009.2033299).","DOI":"10.1109\/TMTT.2009.2033299"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] W. Hu and Y.X. Guo: \u201cAn evolutionary multilayer perceptron-based large-signal model of GaN HEMTs including self-heating and trapping effects,\u201d IEEE Trans. Microw. Theory Techn. <b>70<\/b> (2021) 1146 (DOI: 10.1109\/TMTT.2021.3132892).","DOI":"10.1109\/TMTT.2021.3132892"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] C. Wang, <i>et al<\/i>.: \u201cAn electrothermal model for empirical large-signal modeling of AlGaN\/GaN HEMTs including self-heating and ambient temperature effects,\u201d IEEE Trans. Microw. Theory Techn. <b>62<\/b> (2014) 2878 (DOI: 10.1109\/TMTT.2014.2364821).","DOI":"10.1109\/TMTT.2014.2364821"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] Z. Naghibi, <i>et al<\/i>.: \u201cAdjoint recurrent neural network technique for nonlinear electronic component modeling,\u201d International Journal of Circuit Theory and Applications <b>50<\/b> (2022) 1119 (DOI: 10.1002\/cta.3184).","DOI":"10.1002\/cta.3184"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] A. Morales-Hern\u00e1ndez, <i>et al<\/i>.: \u201cA survey on multi-objective hyperparameter optimization algorithms for machine learning,\u201d Artificial Intelligence Review <b>56<\/b> (2023) 8043 (DOI: 10.1007\/s10462-022-10359-2).","DOI":"10.1007\/s10462-022-10359-2"},{"key":"32","unstructured":"[32] Q.J. Zhang: NeuroModelerPlus_V2.1E, Dept. of Electronics, Carleton University, Ottawa, ON, Canada."},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] L. 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