Takagi Sugeno Kang Fuzzy Elliptic Type-2 CMAC Using Improved Particle Swarm Optimization for Nonlinear Systems

Takagi Sugeno Kang Fuzzy Elliptic Type-2 CMAC Using Improved Particle Swarm Optimization for Nonlinear Systems | 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 Takagi Sugeno Kang Fuzzy Elliptic Type-2 CMAC Using Improved Particle Swarm Optimization for Nonlinear Systems Tuan-Tu Huynh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5831861/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 study aims to develop a new efficient neural network called Takagi Sugeno Kang fuzzy elliptic type-2 cerebellar model articulation controller (TSKFET2C) using improved particle swarm optimization for nonlinear systems. For identification and prediction problems, the TSKFET2C is used as the main identifier and the main predictor, respectively. The learning laws for the system parameters are established for all rules of the proposed structure based on the gradient descent algorithm and the minimization of the cost function. For the control problem, the TSKFET2C is used as the main controller and an auxiliary controller is used to eliminate the residual error. The stability of the control system is guaranteed by Lyapunov theory. Moreover, an improved particle swarm optimization is employed to achieve optimal learning rates for updating the parameters of the TSKFET2C. Finally, the proposed TSKFET2C is applied to three types of nonlinear systems to illustrate its performance and effectiveness. Applied Mathematics Elliptic membership function TSK type-2 fuzzy system cerebellar model articulation controller (CMAC) identification time series prediction chaotic synchronization 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-5831861","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402323271,"identity":"4350bfd1-ea7c-4ffd-8d33-81fb61333228","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 06:49:33","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-5831861/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5831861/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73934408,"identity":"2b744a07-d93a-48a1-9ce6-85205e983b3b","added_by":"auto","created_at":"2025-01-16 06:45:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2024548,"visible":true,"origin":"","legend":"","description":"","filename":"Bai2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5831861/v1_covered_8f19ef4c-0242-435e-a2ca-6c7a00ee44ee.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eTakagi Sugeno Kang Fuzzy Elliptic Type-2 CMAC Using Improved Particle Swarm Optimization for Nonlinear Systems\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 type-2 fuzzy system, cerebellar model articulation controller (CMAC), identification, time series prediction, chaotic synchronization","lastPublishedDoi":"10.21203/rs.3.rs-5831861/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5831861/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to develop a new efficient neural network called Takagi Sugeno Kang fuzzy elliptic type-2 cerebellar model articulation controller (TSKFET2C) using improved particle swarm optimization for nonlinear systems. 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