Functional Link Neural Network with Modified Artificial Bee Colony for Data Classification

نویسندگان

  • Tutut Herawan
  • Yana Mazwin Mohmad Hassim
  • Rozaida Ghazali
چکیده

FunctionalLinkNeuralNetwork(FLNN)hasemergedasanimportanttoolforsolvingnon-linear classificationproblemandhasbeensuccessfullyappliedinmanyengineeringandscientificproblems. TheFLNNstructureismuchmoremodestthanordinaryfeedforwardnetworkliketheMultilayer Perceptron (MLP)due to its flat network architecturewhich employs less tuneableweights for training.However,thestandardBackpropagation(BP)learningusesforFLNNtrainingpronetoget trapinlocalminimawhichaffecttheFLNNclassificationperformance.TorecovertheBP-learning drawback,thispaperproposesanArtificialBeeColony(ABC)optimizationwithmodificationon beeforagingbehaviour(mABC)asanalternativelearningschemeforFLNN.Thisismotivatedby goodexplorationandexploitationcapabilitiesofsearchingoptimalweightparametersexhibitbyABC algorithm.TheresultoftheclassificationaccuracymadebyFLNNwithmABC(FLNN-mABC) iscomparedwiththeoriginalFLNNarchitecturewithstandardBackpropagation(BP)(FLNN-BP) andstandardABCalgorithm(FLNN-ABC).TheFLNN-mABCalgorithmprovidesbetterlearning schemefortheFLNNnetworkwithaverageoverallimprovementof4.29%ascomparedtoFLNNBPandFLNN-ABC. KeywoRDS Artificial Bee Colony Data Mining, Functional Link Neural Network, Learning Scheme

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عنوان ژورنال:
  • IJIIT

دوره 13  شماره 

صفحات  -

تاریخ انتشار 2017