Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/62917
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dc.titleUse of neural fuzzy networks with mixed genetic/gradient algorithm in automated vehicle control
dc.contributor.authorHuang, S.
dc.contributor.authorRen, W.
dc.date.accessioned2014-06-17T06:56:19Z
dc.date.available2014-06-17T06:56:19Z
dc.date.issued1999
dc.identifier.citationHuang, S., Ren, W. (1999). Use of neural fuzzy networks with mixed genetic/gradient algorithm in automated vehicle control. IEEE Transactions on Industrial Electronics 46 (6) : 1090-1102. ScholarBank@NUS Repository.
dc.identifier.issn02780046
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/62917
dc.description.abstractThis paper is concerned with the design of automated vehicle guidance control. First, we propose to implement the guidance tasks using several individual controllers. Next, a neural fuzzy network (NFN) is used to build these controllers, where the NFN constructs are neural-network-based connectionist models. A two-phase hybrid learning algorithm which combines genetic and gradient algorithms is employed to identify the NFN weightings. Finally, simulations are given to show that the proposed technology can improve the speed of learning convergence and enhance the performance of vehicle control.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/41.807993
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.sourcetitleIEEE Transactions on Industrial Electronics
dc.description.volume46
dc.description.issue6
dc.description.page1090-1102
dc.description.codenITIED
dc.identifier.isiut000084080500006
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