{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:36:07Z","timestamp":1760524567033,"version":"3.37.3"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61927804"],"award-info":[{"award-number":["61927804"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"DOI":"10.1007\/s10489-023-05200-4","type":"journal-article","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T08:02:28Z","timestamp":1702540948000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["1-D multi-channel CNN with transfer functions for inverse electromagnetic behaviors modeling and design optimization of high-dimensional filters"],"prefix":"10.1007","author":[{"given":"Yimin","family":"Ren","sequence":"first","affiliation":[]},{"given":"Xiaojiao","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Zhengyang","family":"You","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2935-9514","authenticated-orcid":false,"given":"Xiaoping","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"issue":"5","key":"5200_CR1","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/MMM.2021.3056935","volume":"22","author":"H-J Song","year":"2021","unstructured":"Song H-J (2021) Terahertz wireless communications: recent developments including a prototype system for short-range data downloading. IEEE Microwave Magazine 22(5):88\u201399","journal-title":"IEEE Microwave Magazine"},{"key":"5200_CR2","doi-asserted-by":"crossref","first-page":"107181","DOI":"10.1016\/j.mssp.2022.107181","volume":"154","author":"N Krishna","year":"2023","unstructured":"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","journal-title":"Mater Sci Semicond Process"},{"issue":"11","key":"5200_CR3","doi-asserted-by":"crossref","first-page":"4751","DOI":"10.1109\/TMTT.2022.3200040","volume":"70","author":"C Roy","year":"2022","unstructured":"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\u20134764","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"5","key":"5200_CR4","doi-asserted-by":"crossref","first-page":"2879","DOI":"10.1002\/jnm.2879","volume":"34","author":"W Na","year":"2021","unstructured":"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","journal-title":"Int J Numer Model Electron Netw, Devices and Fields"},{"issue":"22","key":"5200_CR5","doi-asserted-by":"crossref","first-page":"2752","DOI":"10.3390\/electronics10222752","volume":"10","author":"MSI Sagar","year":"2021","unstructured":"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","journal-title":"Electronics"},{"issue":"11","key":"5200_CR6","doi-asserted-by":"crossref","first-page":"4635","DOI":"10.1109\/TMTT.2022.3208898","volume":"70","author":"Y Yu","year":"2022","unstructured":"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\u20134651","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"11","key":"5200_CR7","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1109\/LMWC.2020.3026187","volume":"30","author":"SC Mejillones","year":"2020","unstructured":"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\u20131020","journal-title":"IEEE Microwave and Wireless Components Letters"},{"issue":"1","key":"5200_CR8","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/TMTT.2014.2373365","volume":"63","author":"S Koziel","year":"2014","unstructured":"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\u2013114","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"11","key":"5200_CR9","doi-asserted-by":"crossref","first-page":"4740","DOI":"10.1109\/TMTT.2022.3191327","volume":"70","author":"S Koziel","year":"2022","unstructured":"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\u20134750","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"3","key":"5200_CR10","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1109\/TEC.2020.2982265","volume":"35","author":"E Jansson","year":"2020","unstructured":"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\u20131675","journal-title":"IEEE Transactions on Energy Conversion"},{"key":"5200_CR11","doi-asserted-by":"crossref","unstructured":"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\u201314","DOI":"10.1007\/s10489-023-04638-w"},{"issue":"7","key":"5200_CR12","doi-asserted-by":"crossref","first-page":"5007","DOI":"10.1109\/TAP.2022.3140313","volume":"70","author":"Y-F Liu","year":"2022","unstructured":"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\u20135014","journal-title":"IEEE Transactions on Antennas and Propagation"},{"issue":"11","key":"5200_CR13","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1080\/00207217.2016.1138539","volume":"103","author":"MR Salehi","year":"2016","unstructured":"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\u20131893","journal-title":"Int J Electron"},{"issue":"1","key":"5200_CR14","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/TMTT.2015.2504099","volume":"64","author":"F Feng","year":"2015","unstructured":"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\u201377","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"key":"5200_CR15","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/TMTT.2022.3197751"},{"issue":"10","key":"5200_CR16","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MMM.2021.3095990","volume":"22","author":"F Feng","year":"2021","unstructured":"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\u201350","journal-title":"IEEE Microwave Magazine"},{"issue":"4","key":"5200_CR17","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1109\/TMTT.2003.809179","volume":"51","author":"Q-J Zhang","year":"2003","unstructured":"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\u20131350","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"4","key":"5200_CR18","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1109\/TMTT.2019.2955689","volume":"68","author":"L-Y Xiao","year":"2019","unstructured":"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\u20131269","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"key":"5200_CR19","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1007\/s11276-020-02478-x","volume":"27","author":"SI Yahya","year":"2021","unstructured":"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\u2013676","journal-title":"Wirel Netw"},{"key":"5200_CR20","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1002\/jnm.3157"},{"issue":"3","key":"5200_CR21","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1109\/TMTT.2016.2630059","volume":"65","author":"W Na","year":"2016","unstructured":"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\u2013745","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"10","key":"5200_CR22","doi-asserted-by":"crossref","first-page":"6651","DOI":"10.1109\/TAP.2021.3069543","volume":"69","author":"L-Y Xiao","year":"2021","unstructured":"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\u20136659","journal-title":"IEEE Transactions on Antennas and Propagation"},{"issue":"4","key":"5200_CR23","doi-asserted-by":"crossref","first-page":"22124","DOI":"10.1002\/mmce.22124","volume":"30","author":"A Pietrenko-Dabrowska","year":"2020","unstructured":"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","journal-title":"Int J RF Microw Comput-Aided Eng"},{"issue":"11","key":"5200_CR24","doi-asserted-by":"crossref","first-page":"6283","DOI":"10.1109\/TAP.2018.2869136","volume":"66","author":"TN Kapetanakis","year":"2018","unstructured":"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\u20136290","journal-title":"IEEE Transactions on Antennas and Propagation"},{"issue":"5","key":"5200_CR25","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/LMWC.2020.2986156","volume":"30","author":"G Pan","year":"2020","unstructured":"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\u2013460","journal-title":"IEEE Microwave and Wireless Components Letters"},{"issue":"8","key":"5200_CR26","doi-asserted-by":"crossref","first-page":"3781","DOI":"10.1109\/TMTT.2018.2841889","volume":"66","author":"C Zhang","year":"2018","unstructured":"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\u20133797","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"7","key":"5200_CR27","doi-asserted-by":"crossref","first-page":"3415","DOI":"10.1109\/TMTT.2022.3166151","volume":"70","author":"M Sedaghat","year":"2022","unstructured":"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\u20133427","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"10","key":"5200_CR28","doi-asserted-by":"crossref","first-page":"4140","DOI":"10.1109\/TMTT.2019.2932738","volume":"67","author":"J Jin","year":"2019","unstructured":"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\u20134155","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"11","key":"5200_CR29","doi-asserted-by":"crossref","first-page":"4683","DOI":"10.1109\/TMTT.2022.3161928","volume":"70","author":"Y Wu","year":"2022","unstructured":"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\u20134693","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"11","key":"5200_CR30","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1109\/TMTT.2009.2032476","volume":"57","author":"Y Cao","year":"2009","unstructured":"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\u20132742","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"key":"5200_CR31","doi-asserted-by":"crossref","unstructured":"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\u20133","DOI":"10.1109\/MWSYM.2016.7539963"},{"issue":"6","key":"5200_CR32","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1109\/TMTT.2020.2979445","volume":"68","author":"J Zhang","year":"2020","unstructured":"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 pad\u00e9 via lanczos and em sensitivities. IEEE Transactions on Microwave Theory and Techniques 68(6):2215\u20132233","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"5","key":"5200_CR33","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/LMWC.2022.3146376","volume":"32","author":"J Zhang","year":"2022","unstructured":"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\u2013382","journal-title":"IEEE Microwave and Wireless Components Letters"},{"key":"5200_CR34","doi-asserted-by":"crossref","unstructured":"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\u20134665","DOI":"10.1109\/TMTT.2022.3207761"},{"issue":"7553","key":"5200_CR35","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"5200_CR36","doi-asserted-by":"crossref","first-page":"82273","DOI":"10.1109\/ACCESS.2020.2991890","volume":"8","author":"J Jin","year":"2020","unstructured":"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\u201382285","journal-title":"IEEE Access"},{"issue":"7","key":"5200_CR37","doi-asserted-by":"crossref","first-page":"5165","DOI":"10.1109\/TAP.2022.3188627","volume":"70","author":"Y Zhou","year":"2022","unstructured":"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\u20135178","journal-title":"IEEE Transactions on Antennas and Propagation"},{"issue":"2","key":"5200_CR38","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/LMWC.2018.2888955","volume":"29","author":"R Hongyo","year":"2019","unstructured":"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\u2013148","journal-title":"IEEE Microwave and Wireless Components Letters"},{"issue":"7","key":"5200_CR39","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1109\/LMWC.2021.3078459","volume":"31","author":"S Zhang","year":"2021","unstructured":"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\u2013920","journal-title":"IEEE Microwave and Wireless Components Letters"},{"key":"5200_CR40","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1007\/s10489-020-01801-5","volume":"51","author":"K Bayoudh","year":"2021","unstructured":"Bayoudh K, Hamdaoui F, Mtibaa A (2021) Transfer learning based hybrid 2d\u20133d cnn for traffic sign recognition and semantic road detection applied in advanced driver assistance systems. Appl Intell 51:124\u2013142","journal-title":"Appl Intell"},{"issue":"10","key":"5200_CR41","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","volume":"26","author":"H Lee","year":"2017","unstructured":"Lee H, Kwon H (2017) Going deeper with contextual cnn for hyperspectral image classification. IEEE Transactions on Image Processing 26(10):4843\u20134855","journal-title":"IEEE Transactions on Image Processing"},{"issue":"11","key":"5200_CR42","doi-asserted-by":"crossref","first-page":"14249","DOI":"10.1007\/s10489-022-04221-9","volume":"53","author":"S Soni","year":"2023","unstructured":"Soni S, Chouhan SS, Rathore SS (2023) Textconvonet: a convolutional neural network based architecture for text classification. Appl Intell 53(11):14249\u201314268","journal-title":"Appl Intell"},{"issue":"7","key":"5200_CR43","doi-asserted-by":"crossref","first-page":"8354","DOI":"10.1007\/s10489-022-03910-9","volume":"53","author":"K Ma","year":"2023","unstructured":"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\u20138369","journal-title":"Appl Intell"},{"key":"5200_CR44","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s10489-018-1179-1","volume":"49","author":"UR Acharya","year":"2019","unstructured":"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\u201327","journal-title":"Appl Intell"},{"issue":"3","key":"5200_CR45","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1109\/TAP.2018.2885437","volume":"67","author":"L Li","year":"2018","unstructured":"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\u20131825","journal-title":"IEEE Transactions on Antennas and Propagation"},{"issue":"9","key":"5200_CR46","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/LMWC.2022.3162414","volume":"32","author":"H-Y Luo","year":"2022","unstructured":"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\u20131022","journal-title":"IEEE Microwave and Wireless Components Letters"},{"issue":"12","key":"5200_CR47","doi-asserted-by":"crossref","first-page":"5359","DOI":"10.1109\/TMTT.2022.3217138","volume":"70","author":"M Desai","year":"2022","unstructured":"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\u20135368","journal-title":"IEEE Transactions on Microwave Theory and Techniques"},{"issue":"2","key":"5200_CR48","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/LMWC.2022.3214467","volume":"33","author":"J Ma","year":"2022","unstructured":"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\u2013129","journal-title":"IEEE Microwave and Wireless Technology Letters"},{"key":"5200_CR49","doi-asserted-by":"crossref","unstructured":"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","DOI":"10.1109\/TMTT.2023.3238418"},{"issue":"12","key":"5200_CR50","doi-asserted-by":"crossref","first-page":"16041","DOI":"10.1007\/s10489-022-04342-1","volume":"53","author":"S Zhang","year":"2023","unstructured":"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\u201316058","journal-title":"Appl Intell"},{"issue":"3","key":"5200_CR51","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","volume":"63","author":"S Kiranyaz","year":"2015","unstructured":"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\u2013675","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"5200_CR52","doi-asserted-by":"crossref","first-page":"107398","DOI":"10.1016\/j.ymssp.2020.107398","volume":"151","author":"S Kiranyaz","year":"2021","unstructured":"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","journal-title":"Mech Syst Signal Process"},{"issue":"4","key":"5200_CR53","doi-asserted-by":"crossref","first-page":"4448","DOI":"10.1007\/s10489-022-03773-0","volume":"53","author":"Y Yin","year":"2023","unstructured":"Yin Y, Wang S, Zhou J (2023) Multisensor-based tool wear diagnosis using 1d-cnn and dgcca. Appl Intell 53(4):4448\u20134461","journal-title":"Appl Intell"},{"issue":"2","key":"5200_CR54","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1109\/LMWC.2022.3208355","volume":"33","author":"C Yu","year":"2022","unstructured":"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\u2013125","journal-title":"IEEE Microwave and Wireless Technology Letters"},{"issue":"3","key":"5200_CR55","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1109\/61.772353","volume":"14","author":"B Gustavsen","year":"1999","unstructured":"Gustavsen B, Semlyen A (1999) Rational approximation of frequency domain responses by vector fitting. IEEE Transactions on Power Delivery 14(3):1052\u20131061","journal-title":"IEEE Transactions on Power Delivery"},{"key":"5200_CR56","doi-asserted-by":"crossref","unstructured":"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\u20139, Boston, MA, USA","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"5200_CR57","unstructured":"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\u2013456"},{"issue":"1","key":"5200_CR58","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"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\u20131958","journal-title":"J Mach Learn Res"},{"key":"5200_CR59","volume-title":"Understanding industrial designed experiments","author":"SR Schmidt","year":"1989","unstructured":"Schmidt SR, Launsby RG (1989) Understanding industrial designed experiments. Air Academy Press, USA"},{"key":"5200_CR60","doi-asserted-by":"crossref","first-page":"93922","DOI":"10.1109\/ACCESS.2020.2990157","volume":"8","author":"Z Zhao","year":"2020","unstructured":"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\u201393938","journal-title":"IEEE Access"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05200-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-05200-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05200-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T08:49:40Z","timestamp":1706172580000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-05200-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,14]]},"references-count":60,"alternative-id":["5200"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-05200-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,12,14]]},"assertion":[{"value":"26 November 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"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.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}