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Behavioral learning of aircraft landing sequencing using a society of Probabilistic Finite state Machines

contributor authorJiangjun Tang , Abbass, H.A.
date accessioned2020-03-12T23:35:35Z
date available2020-03-12T23:35:35Z
date issued2014
identifier other6900597.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/1117211?locale-attribute=en&show=full
formatgeneral
languageEnglish
publisherIEEE
titleBehavioral learning of aircraft landing sequencing using a society of Probabilistic Finite state Machines
typeConference Paper
contenttypeMetadata Only
identifier padid8286938
subject keywordsAdders
subject keywordsCMOS integrated circuits
subject keywordsDelays
subject keywordsLogic gates
subject keywordsPower demand
subject keywordsThreshold voltage
subject keywordsTransistors
subject keywordsMixed Threshold Voltage (MVT) scheme
subject keywordsModified Gate Diffusion Input (GDI) technique
subject keywordsarea
subject keywordsenergy delay product (EDP)
subject keywordshigh speed
subject keywordslow power
subject keywordspower delay product (PDP)
subject keywordstransistor count
identifier doi10.1109/ICCICCT.2014.6992926
journal titlevolutionary Computation (CEC), 2014 IEEE Congress on
filesize1589001
citations0


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