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dc.contributor.authorCHEN, FCen_US
dc.contributor.authorLIU, CCen_US
dc.date.accessioned2014-12-08T15:03:57Z-
dc.date.available2014-12-08T15:03:57Z-
dc.date.issued1994-06-01en_US
dc.identifier.issn0018-9286en_US
dc.identifier.urihttp://dx.doi.org/10.1109/9.293202en_US
dc.identifier.urihttp://hdl.handle.net/11536/2465-
dc.description.abstractMultilayer neural networks are used in a nonlinear adaptive control problem. The plant is an unknown feedback-linearizable continuous-time system. The control law is defined in terms of the neural network models of system nonlinearities to control the plant to track a reference command. The network parameters are updated on-line according to a gradient learning rule with dead zone. A local convergence result is provided, which says that if the initial parameter errors are small enough, then the tracking error will converge to a bounded area. Simulations are designed to demonstrate various aspects of theoretical results.en_US
dc.language.isoen_USen_US
dc.titleADAPTIVELY CONTROLLING NONLINEAR CONTINUOUS-TIME SYSTEMS USING MULTILAYER NEURAL NETWORKSen_US
dc.typeNoteen_US
dc.identifier.doi10.1109/9.293202en_US
dc.identifier.journalIEEE TRANSACTIONS ON AUTOMATIC CONTROLen_US
dc.citation.volume39en_US
dc.citation.issue6en_US
dc.citation.spage1306en_US
dc.citation.epage1310en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:A1994NU01800028-
dc.citation.woscount179-
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