基于神經(jīng)網(wǎng)絡(luò)的抗彎曲低損耗光纖設(shè)計(jì)及其布里淵散射特性研究

基于神經(jīng)網(wǎng)絡(luò)的抗彎曲低損耗光纖設(shè)計(jì)及其布里淵散射特性研究基于神經(jīng)網(wǎng)絡(luò)的抗彎曲低損耗光纖設(shè)計(jì)及其布里淵散射特性研究

摘要：隨著信息傳輸量的迅速增長(zhǎng)，光纖通信系統(tǒng)的需求也愈加突出。然而，在實(shí)際應(yīng)用中，光纖由于外界因素的影響，比如彎曲、壓力等，容易引起光信號(hào)傳輸?shù)膿p耗和失真，進(jìn)而降低通信質(zhì)量。因此，本文提出一種基于神經(jīng)網(wǎng)絡(luò)的抗彎曲低損耗光纖設(shè)計(jì)方法，并探究其布里淵散射特性。該方法采用神經(jīng)網(wǎng)絡(luò)自適應(yīng)調(diào)整光纖傳輸時(shí)受到的彎曲影響，使其具備更好的抗彎曲能力和低損耗。同時(shí)，通過仿真實(shí)驗(yàn)研究光纖在不同彎曲半徑下的布里淵散射特性，并提出了優(yōu)化方案，從而進(jìn)一步提升光纖的通信性能。實(shí)驗(yàn)結(jié)果表明，該光纖具有較高的信號(hào)傳輸質(zhì)量和穩(wěn)定性，并可在實(shí)際通信中得到廣泛應(yīng)用。

關(guān)鍵詞：光纖通信；神經(jīng)網(wǎng)絡(luò)；抗彎曲能力；低損耗；布里淵散射

Abstract:Withtherapidgrowthofinformationtransmission,thedemandforfiberopticcommunicationsystemsisalsobecomingincreasinglyprominent.However,inpracticalapplications,fiberopticiseasilyaffectedbyexternalfactorssuchasbendingandpressure,whichcancausesignaltransmissionlossesanddistortion,therebyreducingcommunicationquality.Therefore,thispaperproposesafiberopticdesignmethodbasedonneuralnetworkthatcanresistbendingandlowloss,andexploresitsBrillouinscatteringcharacteristics.Thismethodusesneuralnetworktoadaptivelyadjustthebendingeffectsonfiberoptictransmission,makingitmoreresistanttobendingandlowloss.Atthesametime,thefiberoptic'sBrillouinscatteringcharacteristicsarestudiedthroughsimulationexperimentsatdifferentbendingradii,andoptimizationsolutionsareproposedtofurtherimprovethecommunicationperformanceoffiberoptic.Theexperimentalresultsshowthatthisfiberoptichashighsignaltransmissionqualityandstability,andcanbewidelyusedinpracticalcommunication.

Keywords:fiberopticcommunication;neuralnetwork;resistancetobending;lowloss;BrillouinscatteringFiberopticcommunicationhasbecomeanessentialtoolinmoderncommunicationnetworksduetoitshighbandwidthandlowsignalloss.Oneofthecriticalfactorsthataffecttheperformanceoffiberopticcommunicationisthefiberoptic'sresistancetobending.Thebendingofthefiberopticcanresultinhighsignalattenuation,leadingtoadecreaseinthesignalqualityandstabilityofthecommunicationnetwork.

Toimprovetheresistancetobendingofthefiberoptic,researchershaveconductednumerousstudiesandproposedseveralsolutions.OneoftheeffectivemethodsistooptimizetheBrillouinscatteringcharacteristicsofthefiberoptic.Brillouinscatteringisaphenomenonthatoccursinopticalfiber,wherethescatteredlightinteractswiththeacousticphononsinthefiber,resultinginashiftinthefrequencyofthescatteredlight.Thisshiftcanbeusedtomeasurethetemperatureandstraininthefiberoptic.

Throughsimulationexperiments,theBrillouinscatteringcharacteristicsofthefiberopticatdifferentbendingradiiarestudied.TheresultsshowthattheBrillouinshiftandlinewidthofthefiberopticaresignificantlyaffectedbythebendingradius.Toimprovethebendingresistanceofthefiberoptic,theresearcherssuggestoptimizingtheBrillouinscatteringcharacteristicsbyadjustingthefiberoptic'sparameters,suchasitscoresizeanddopantconcentration.

Furthermore,aneuralnetwork-basedapproachisalsoproposedtoenhancethefiberoptic'sresistancetobending.TheneuralnetworkistrainedusingtheexperimentaldatatopredicttheBrillouinshiftandlinewidthofthefiberopticfordifferentbendingradii.Byincorporatingtheneuralnetworkintothecommunicationsystem,thefiberopticcanadapttodifferentbendingradii,ensuringhighsignaltransmissionqualityandstability.

Inconclusion,theresistancetobendingofthefiberopticisacrucialfactorfortheperformanceoffiberopticcommunicationnetworks.Throughsimulationexperimentsandoptimizationsolutions,theBrillouinscatteringcharacteristicsofthefiberopticcanbeimproved,resultinginhighsignaltransmissionqualityandstability.Theproposedneuralnetwork-basedapproachcanfurtherenhancethefiberoptic'sresistancetobending,ensuringefficientandreliablecommunicationFiberopticcommunicationnetworkshaverevolutionizedthewaywecommunicateandshareinformation.Theuseofopticalfibershasmadeitpossibletotransmitvastamountsofdataathighspeeds,overlongdistancesandwithminimallosses.

However,theperformanceoffiberopticsystemscanbesignificantlyaffectedbyexternalfactorssuchastemperatureandmechanicalstress.Oneofthemostcriticalfactorsaffectingtheperformanceoffiberopticcablesistheirresistancetobending.

Bendingcausesthefiberopticcabletolosesomeofitslightcarryingcapacity,leadingtosignallossanddistortion.Thebending-inducedsignaldegradationcansignificantlyreducetheefficiencyandreliabilityofthecommunicationsystems.

Therefore,itisessentialtodevelopapproachestoenhancethefiberoptic'sresistancetobending.OneofthemethodsthathavebeenusedtoovercomethischallengeisBrillouinscattering.

Brillouinscatteringisaphenomenonthatoccurswhenlightinteractswiththeacousticwaveswithinafiberopticcable.Theacousticwavesgenerateashiftintheopticalfrequency,whichcanbeusedtodeterminethefiber'smechanicalproperties.

Throughsimulationexperimentsandoptimizationsolutions,theBrillouinscatteringcharacteristicsofafiberopticcablecanbeanalyzedandimproved.Thisapproachcansignificantlyenhancethefiber'sresistancetobending,resultinginhighsignaltransmissionqualityandstability.

Recently,researchershaveproposedusingmachinelearningalgorithms,specificallyneuralnetworks,toimprovetheresistanceoffiberopticcablestobending.Neuralnetworksareatypeofmachinelearningalgorithmthatcanlearnfromlargeamountsofdataandidentifypatternsandcorrelations.

Byanalyzinglargedatasetsoffiberopticcablecharacteristicsandtheirbendingbehaviors,neuralnetworkscanlearntopredictthefiber'sresistancetobendingaccurately.Thisapproachcanbeusedtooptimizethefiber'sdesignandcomposition,resultinginimprovedresistancetobendingandenhancedcommunicationsystemperformance.

Inconclusion,theresistancetobendingisacriticalfactorfortheperformanceoffiberopticcommunicationnetworks.UsingtheBrillouinscatteringapproachandmachinelearningalgorithms,wecananalyzeandoptimizethefiber'scharacteristicstoenhanceitsresistancetobending.Improvedresistancetobendingensuresefficientandreliablecommunication,criticalformoderncommunicationsystemsFurthermore,theroleoffiberopticsensorshasbeengainingpopularityinvariousfieldssuchasstructuralhealthmonitoring,medicaldiagnosis,andenvironmentalmonitoring.Thesesensorsrelyonthebendingofthefibertodetectchangesinparameterssuchastemperature,pressure,andstrain.Therefore,enhancingtheresistancetobendingofthefiberopticsensoriscrucialtoensureitsaccuracyandreliability.

Apartfromthefiber'smaterialcharacteristics,themanufacturingprocessalsoplaysasignificantroleindeterminingitsresistancetobending.Theprocessinvolvesseveralstepssuchaspreformfabrication,drawingofthefiber,andcoating.Eachofthesestepscanaffectthefiber'smicrostructure,whichinturnaffectsitsproperties.Forinstance,excessivetensionduringthedrawingprocesscanintroducedefectsinthefiber,reducingitsbendingresistance.

Toimprovethemanufacturingprocessandoptimizethefiber'sproperties,researchershavebeenusingvarioustechniquessuchasnumericalsimulationsandexperimentalcharacterization.Numericalsimulationsinvolveusingcomputermodelstosimulatethefiber'sbehaviorunderdifferentconditions,suchasbendingandtemperaturechanges.Thesesimulationshelpidentifythecriticalfactorsthataffectthefiber'sperformanceandoptimizethemanufacturingprocess.

Experimentalcharacterizationinvolvesanalyzingthefiber'spropertiesundervariousconditions,suchasmeasuringitsbendingstiffnessandstress-strainbehavior.Theseexperimentshelpvalidatethenumericalsimulationsandprovideadditionalinsightsintothefiber'sbehavior.

Inconclusion,theresistancetobendingisacriticalfactorindeterminingtheperformance