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dc.contributor.authorChiou, YCen_US
dc.contributor.authorLan, LWen_US
dc.date.accessioned2014-12-08T15:18:52Z-
dc.date.available2014-12-08T15:18:52Z-
dc.date.issued2005-06-16en_US
dc.identifier.issn0165-0114en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.fss.2004.11.011en_US
dc.identifier.urihttp://hdl.handle.net/11536/13576-
dc.description.abstractLogic rules and membership functions are two key components of a fuzzy logic controller (FLC). If only one component is learned, the other one is often set subjectively thus can reduce the applicability of FLC. If both components are learned simultaneously, a very long chromosome is often needed thus may deteriorate the learning performance. To avoid these shortcomings, this paper employs genetic algorithms to learn both logic rules and membership functions sequentially. We propose a bi-level iterative evolution algorithm in selecting the logic rules and tuning the membership functions for a genetic fuzzy logic controller (GFLC). The upper level is to solve the composition of logic rules using the membership functions tuned by the lower level. The lower level is to determine the shape of membership functions using the logic rules learned from the upper level. We also propose a new encoding method for tuning the membership functions to overcome the problem of too many constraints. Our proposed GFLC model is compared with other similar GFLC, artificial neural network and fuzzy neural network models, which are trained and validated by the same examples with theoretical and field-observed car-following behaviors. The results reveal that our proposed GFLC has outperformed. © 2004 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectgenetic algorithmsen_US
dc.subjectgenetic fuzzy logic controlleren_US
dc.subjectartificial neural networken_US
dc.subjectfuzzy neural networken_US
dc.subjectcar-following behaviorsen_US
dc.titleGenetic fuzzy logic controller: an iterative evolution algorithm with new encoding methoden_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.fss.2004.11.011en_US
dc.identifier.journalFUZZY SETS AND SYSTEMSen_US
dc.citation.volume152en_US
dc.citation.issue3en_US
dc.citation.spage617en_US
dc.citation.epage635en_US
dc.contributor.department運輸與物流管理系 註:原交通所+運管所zh_TW
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.identifier.wosnumberWOS:000229063500013-
dc.citation.woscount22-
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