Neural network processing for adaptive line enhancement-中国期刊网

摘要 Thispaperdescribestheinverstigationdevotedtoestablishsuitableweightsinafeed-forwardneuralnetworkrealizingthenarrow-bandfilteringmapinthecaseofadaptivelineenhancement(ALE)bytheutilityoftheoptimumcommonlearningratebackpropagation(OCLRBP)algorithm.Itisfoundthatafeed-forwardnetworkwith64linearinputandoutputneurons,and8oddsigmoidneuronsinthehiddenlayer,i.e.an(64→8→64)architecture,couldestablishthespecificinput-outputfunctioninthecaseofrelativelylowsignal-to-noiseradio.Onlyisaninputsignalconsistingofmixedperiodicandbroad-bandcomponentsavailabletothenetworksystem.Afterlearning,boththe"fanning-in-connectionpatterns",eachofwhichconsistsofweightsfanningintoahidden-neuronFromalltheoutputsofinput-neurons,andthe"fanning-out-connectionpatterns",eachofwhichconsistsofweightsfanningoutfromahidden-neurontoalltheinputsofoutput-neurons,aretunedtotheperiodicsignals.Thenonline

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Neural network processing for adaptive line enhancement

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Neural network processing for adaptive line enhancement

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