Research on Human Height-Weight Prediction by Distributed Robust Adaptive Fault-tolerant Optimal Control based on DNN

Research on Human Height-Weight Prediction by Distributed Robust Adaptive Fault-tolerant Optimal Control based on DNN | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research on Human Height-Weight Prediction by Distributed Robust Adaptive Fault-tolerant Optimal Control based on DNN Zhifang Wang, Quanzhen Huang, Jianguo Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7621760/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract In this paper, a distributed robust adaptive confined fault-tolerant optimal control method based on deep neural networks is proposed, aiming to solve the complexity and uncertainty problems in human height and weight prediction. In the field of public security technology, accurate prediction of individual physiological characteristics has important application value, especially in crime prevention, individual identification, and behavior analysis. Traditional prediction methods often perform erratically in the face of data noise, environmental changes, and outliers. To this end, this paper combines deep learning and fault-tolerant control theory to propose an efficient and reliable prediction framework by optimizing the robustness and adaptive ability of the control model. By introducing a limited fault-tolerant mechanism, it can maintain high prediction accuracy and stability under various perturbations and incomplete data conditions. Simulation and experimental results show that after 2000 rounds of iterative optimization, the normal and fault-tolerant prediction accuracies of human height for finger length of left and right hands are 98.4% and 97.7%, respectively, and the normal and fault-tolerant prediction accuracies of human body weight are 98.2% and 97.5%, respectively, by combining the 372 sets of data with 30% of data loss caused by human. The accuracy of normal and fault-tolerant prediction of human height was 90.8% and 89.2% for the finger length of the left hand, and the accuracy of normal and fault-tolerant prediction of human weight was 85.6% and 83.3%, respectively. The normal and fault-tolerant prediction accuracies of human height for the finger length of the right hand were 96% and 95.3%, and the normal and fault-tolerant prediction accuracies of human weight were 94.4% and 93.5%, respectively. This study provides a new idea and technical path for biometric prediction and analysis in the field of public security technology, which has important theoretical significance and practical value. Physical sciences/Engineering Physical sciences/Mathematics and computing DNN Distributed Control Robust Adaptive Control Limit Fault Tolerant Optimization Human Biometric Prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Feb, 2026 Reviews received at journal 05 Feb, 2026 Reviews received at journal 28 Jan, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers invited by journal 27 Jan, 2026 Editor assigned by journal 20 Jan, 2026 Editor invited by journal 24 Sep, 2025 Submission checks completed at journal 23 Sep, 2025 First submitted to journal 18 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7621760","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":581840205,"identity":"d531f9ae-8d1c-4053-b047-2ad57e3a2510","order_by":0,"name":"Zhifang Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACfmbGxgcJBhI8bOzNx4jTItne3GzwocBGho/nWBqQb0BYi8GZ422SMz6k2chJ5JgRp4XhRmKzMY/BYR42njPfHvPu+MPAP7sBvw7GGYmNj8Fa2Hu3G/OeMWCQuHMAvxZmCbgtZ7dJ87YZMBhIJODXwiaR2CYN1iKR84w4LTw8B4HeN0gDaWEjTosEeyMwkA1sgA47Zm4494wxj8QNAlrsD7M/fJDwR8Jevr352YO3O+Tk+GcQ0IIKGBsYeEhRD9EyCkbBKBgFowADAABB8j/zO2LmvQAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Zhifang","middleName":"","lastName":"Wang","suffix":""},{"id":581840206,"identity":"9ec6843d-0e42-45e3-ba33-d1ff516b2e90","order_by":1,"name":"Quanzhen Huang","email":"","orcid":"","institution":"Henan University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Quanzhen","middleName":"","lastName":"Huang","suffix":""},{"id":581840207,"identity":"3a578558-254d-450b-bc6d-d2df8587631f","order_by":2,"name":"Jianguo Yu","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Jianguo","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-09-15 14:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7621760/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7621760/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101388087,"identity":"67510702-6b7c-4b43-9047-0f2a90b6223d","added_by":"auto","created_at":"2026-01-29 07:42:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":964622,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7621760/v1_covered_2ecbdb48-9268-4e8e-9736-9cb614a7db64.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Human Height-Weight Prediction by Distributed Robust Adaptive Fault-tolerant Optimal Control based on DNN","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"DNN, Distributed Control, Robust Adaptive Control, Limit Fault Tolerant Optimization, Human Biometric Prediction","lastPublishedDoi":"10.21203/rs.3.rs-7621760/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7621760/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this paper, a distributed robust adaptive confined fault-tolerant optimal control method based on deep neural networks is proposed, aiming to solve the complexity and uncertainty problems in human height and weight prediction. In the field of public security technology, accurate prediction of individual physiological characteristics has important application value, especially in crime prevention, individual identification, and behavior analysis. Traditional prediction methods often perform erratically in the face of data noise, environmental changes, and outliers. To this end, this paper combines deep learning and fault-tolerant control theory to propose an efficient and reliable prediction framework by optimizing the robustness and adaptive ability of the control model. By introducing a limited fault-tolerant mechanism, it can maintain high prediction accuracy and stability under various perturbations and incomplete data conditions. Simulation and experimental results show that after 2000 rounds of iterative optimization, the normal and fault-tolerant prediction accuracies of human height for finger length of left and right hands are 98.4% and 97.7%, respectively, and the normal and fault-tolerant prediction accuracies of human body weight are 98.2% and 97.5%, respectively, by combining the 372 sets of data with 30% of data loss caused by human. The accuracy of normal and fault-tolerant prediction of human height was 90.8% and 89.2% for the finger length of the left hand, and the accuracy of normal and fault-tolerant prediction of human weight was 85.6% and 83.3%, respectively. The normal and fault-tolerant prediction accuracies of human height for the finger length of the right hand were 96% and 95.3%, and the normal and fault-tolerant prediction accuracies of human weight were 94.4% and 93.5%, respectively. This study provides a new idea and technical path for biometric prediction and analysis in the field of public security technology, which has important theoretical significance and practical value.","manuscriptTitle":"Research on Human Height-Weight Prediction by Distributed Robust Adaptive Fault-tolerant Optimal Control based on DNN","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 07:40:12","doi":"10.21203/rs.3.rs-7621760/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-12T09:17:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T02:24:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-28T13:35:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83792178476911913599658515467764903851","date":"2026-01-27T14:55:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105145394735642891624696580160630369946","date":"2026-01-27T13:40:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-27T10:39:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-20T13:01:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-24T04:32:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-23T05:37:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-18T14:09:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f035c8a-3c44-45cd-9765-35ad065b8dd1","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61902230,"name":"Physical sciences/Engineering"},{"id":61902231,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-21T04:56:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 07:40:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7621760","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7621760","identity":"rs-7621760","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}