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dc.contributor.authorJena, Pradyot Ranjan-
dc.contributor.authorMajhi, Ritanjali-
dc.contributor.authorMajhi, Babita-
dc.date.accessioned2024-12-27T06:25:39Z-
dc.date.available2024-12-27T06:25:39Z-
dc.date.issued2015-
dc.identifier.citation10.1016/j.jksuci.2015.01.002en_US
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/2142-
dc.descriptionNITWen_US
dc.description.abstractThispaperpresentsanewadaptiveforecastingmodelusingaknowledgeguidedartificial neuralnetwork(KGANN)structureforefficientpredictionofexchangerate.Thenewstructurehas twoparallel systems.Thefirst systemisaleastmeansquare(LMS) trainedadaptive linearcombiner,whereas thesecondsystememploysanadaptiveFLANNmodel tosupplement theknowledgebasewithanobjective to improve its performance value. Theoutput of a trainedLMS model isaddedtoanadaptiveFLANNmodeltoprovideamoreaccurateexchangeratecompared tothatpredictedbyeitherasimpleLMSoraFLANNmodel.Thisfindinghasbeendemonstrated throughanexhaustingcomputer simulationstudyandusing real lifedata.Thus theproposed KGANNisanefficient forecastingmodel forexchangerateprediction.en_US
dc.language.isoenen_US
dc.publisherJournal of King Saud University - Computer and Information Sciencesen_US
dc.subjectArtificial neural networken_US
dc.subjectNeural network(FLANN);en_US
dc.titleDevelopment and performance evaluation of a novel knowledge guided artificial neural network (KGANN) model for exchange rate predictionen_US
dc.typeArticleen_US
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