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To address this challenge, this study proposes an enhanced least squares support vector regression (LSSVR) method\u2014termed BO-VSV-LSSVR\u2014which integrates virtual support vector data augmentation (VSV-DA) and Bayesian optimization (BO). In the proposed framework, high-confidence virtual training samples are generated around support vectors using Gaussian perturbation and weighted neighborhood interpolation, effectively alleviating data sparsity. Additionally, BO is employed to adaptively tune model hyperparameters, further improving predictive performance. Experimental evaluations using multiple real-flight datasets from a small unmanned helicopter validate that the BO-VSV-LSSVR model outperforms conventional methods in both prediction accuracy and generalization capability. This work offers a high-precision modeling framework for small unmanned helicopter control and contributes to the broader development of data augmentation and intelligent optimization techniques in regression-based modeling.<\/jats:p>","DOI":"10.2514\/1.i011704","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:31:33Z","timestamp":1777631493000},"page":"529-536","update-policy":"https:\/\/doi.org\/10.2514\/aiaa_crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Virtual Support Vector Augmentation and Bayesian Optimization for Small Unmanned Helicopter Modeling"],"prefix":"10.2514","volume":"23","author":[{"given":"Junyi","family":"Shi","sequence":"first","affiliation":[{"name":"Xiamen Nanyang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1436-8119","authenticated-orcid":false,"given":"Jian","family":"Zhou","sequence":"additional","affiliation":[{"name":"Xi\u2019an Polytechnic University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinzhe","family":"Lyu","sequence":"additional","affiliation":[{"name":"Xi\u2019an Polytechnic University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0306-5101","authenticated-orcid":false,"given":"Jian","family":"Lu","sequence":"additional","affiliation":[{"name":"Xi\u2019an Polytechnic University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1387","reference":[{"key":"r1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2022.109205"},{"key":"r2","doi-asserted-by":"publisher","DOI":"10.1002\/asjc.2963"},{"key":"r3","doi-asserted-by":"publisher","DOI":"10.3390\/app11125331"},{"key":"r4","doi-asserted-by":"publisher","DOI":"10.3390\/rs13122396"},{"key":"r5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119778"},{"key":"r6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107598"},{"key":"r7","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.128782"},{"key":"r8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2022.135716"},{"issue":"6","key":"r9","first-page":"42","volume":"15","author":"Kim S.","year":"2021","journal-title":"Journal of Aerospace System Engineering"},{"key":"r10","doi-asserted-by":"publisher","DOI":"10.1109\/MAES.2023.3335333"},{"key":"r11","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2023.129589"},{"key":"r12","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10212689"},{"key":"r13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107055"},{"key":"r14","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3199712"},{"key":"r15","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"r16","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.11192"},{"key":"r17","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"r18","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"r19","doi-asserted-by":"publisher","DOI":"10.36548\/jscp.2021.1.003"},{"key":"r20","doi-asserted-by":"publisher","DOI":"10.2514\/1.I010891"},{"key":"r21","doi-asserted-by":"publisher","DOI":"10.2514\/1.I011395"},{"key":"r22","doi-asserted-by":"publisher","DOI":"10.2514\/1.I010964"},{"key":"r23","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106955"},{"key":"r24","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05425-2"},{"key":"r25","doi-asserted-by":"publisher","DOI":"10.1142\/S0219876221500249"},{"key":"r26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.01.010"},{"key":"r27","unstructured":"TurnerR.ErikssonD.McCourtM.KiiliJ.LaaksonenE.XuZ.GuyonI. \u201cBayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge,\u201d Proceedings of the NeurIPS 2020 Competition and Demonstration Track, Proceedings of Machine Learning Research, edited by EscalanteH. 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