A New Multilayer Takagi Sugeno Kang Elliptic Type- 2 Fuzzy Cerebellar-Imitated Neural Network for Nonlinear Systems Using Modified Grey-Wolf Algorithm

A New Multilayer Takagi Sugeno Kang Elliptic Type- 2 Fuzzy Cerebellar-Imitated Neural Network for Nonlinear Systems Using Modified Grey-Wolf Algorithm | 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 Research Article A New Multilayer Takagi Sugeno Kang Elliptic Type- 2 Fuzzy Cerebellar-Imitated Neural Network for Nonlinear Systems Using Modified Grey-Wolf Algorithm Tuan-Tu Huynh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5831752/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper aims to develop a new efficient multilayer neural network structure by combining the elliptic type-2 membership function with a Takagi- Sugenno-Kang (TSK) fuzzy system and a cerebellar model articulation controller to create a new multilayer TSK elliptic type-2 fuzzy cerebellar-imitated neural network (MTET2FCNN). The MTET2FCNN can be used as the main controller in the control problem, and it is also respectively used as the main identifier and predictor for identification and prediction problems. In addition, a modified Grey-Wolf optimization algorithm is used to update the optimal learning rates of the MTET2FCNN structure. System parameter learning is performed for all rules of the proposed MTET2FCNN structure based on the gradient descent algorithm and cost function minimization. The robust design of the compensator and the system's stability are proved by Lyapunov theory. The synchronization of a chaotic 4D Rabinovich system, the identification of a time-varying system, and a Mackey-Glass time series prediction demonstrate the performance and effectiveness of the proposed system. Applied Mathematics Elliptic membership function TSK fuzzy system neural network Grey-Wolf optimizer chaos synchronization identification time series prediction Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5831752","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402321650,"identity":"e0663fd8-da7e-479f-9892-ad28be359843","order_by":0,"name":"Tuan-Tu Huynh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACAyBmhrIZHzxsANEJxGgBKWJjYDZIJFULmwRRWswlcsweF/44LG8u32NWkbjjMAM/e44BM+8O3FosZ+SYG89IOGy4s43H7EbimcMMkj1vgFrO4HHYjRwzaZ6Ew4wbjoG0tB0GiRgwzmwjrMUepKUApMWeWC2JIC0MYFskcgwYPuLTcuZZmTRPWnryhmNpxRKJbek8EmeeFRzAq+V48jZpHhtr2w2HD2/88LHNWo6/PXnjg0Q8WhgYOAxQuDwg4gA+DQwM7A/wy4+CUTAKRsEoAAAc+VEFQzz6rgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4575-5952","institution":"Lac Hong University","correspondingAuthor":true,"prefix":"","firstName":"Tuan-Tu","middleName":"","lastName":"Huynh","suffix":""}],"badges":[],"createdAt":"2025-01-15 06:37:11","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5831752/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5831752/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73934256,"identity":"ba08f2f0-a37f-462d-a890-ec99c6d60f2a","added_by":"auto","created_at":"2025-01-16 06:39:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4717728,"visible":true,"origin":"","legend":"","description":"","filename":"Bai1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5831752/v1_covered_8b102949-0c10-4cd6-9c5c-8e4811a7bc34.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA New Multilayer Takagi Sugeno Kang Elliptic Type- 2 Fuzzy Cerebellar-Imitated Neural Network for Nonlinear Systems Using Modified Grey-Wolf Algorithm\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Elliptic membership function, TSK fuzzy system, neural network, Grey-Wolf optimizer, chaos synchronization, identification, time series prediction","lastPublishedDoi":"10.21203/rs.3.rs-5831752/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5831752/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eThis paper aims to develop a new efficient multilayer neural network structure by combining the elliptic type-2 membership function with a Takagi- Sugenno-Kang (TSK) fuzzy system and a cerebellar model articulation controller to create a new multilayer TSK elliptic type-2 fuzzy cerebellar-imitated neural network (MTET2FCNN). 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