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Adaptive traffic lights based on hybrid of neural network and genetic algorithm for reduced traffic congestion

contributor authorKaur, T. , Agrawal, S.
date accessioned2020-03-12T19:40:04Z
date available2020-03-12T19:40:04Z
date issued2014
identifier other6799655.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/985509?show=full
formatgeneral
languageEnglish
publisherIEEE
titleAdaptive traffic lights based on hybrid of neural network and genetic algorithm for reduced traffic congestion
typeConference Paper
contenttypeMetadata Only
identifier padid8101156
subject keywordsKalman filters
subject keywordsbattery powered vehicles
subject keywordselectric charge
subject keywordsgenetic algorithms
subject keywordsnonlinear filters
subject keywordssecondary cells
subject keywordsSoC trajectory
subject keywordselectric vehicles
subject keywordsextended Kalman filter
subject keywordsgenetic algorithm
subject keywordslithium-ion battery
subject keywordslumped parameter model
subject keywordsmodel error reduction
subject keywordspolarization time constant optimization
subject keywordsreal-time battery current measurement
subject keywordsreal-time battery voltage measurement
subject keywordsstate-of-charge estimation
subject keywordsAccuracy
subject keywordsBatteries
subject keywordsEquations
subject keywordsEstimation
subject keywordsIntegrated circuit modeling
subject keywordsMathematical model
identifier doi10.1109/ITEC-AP.2014.6941260
journal titlengineering and Computational Sciences (RAECS), 2014 Recent Advances in
filesize224874
citations0


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