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基于遗传优化的BP神经网络法在甲烷检测中的应用.docx

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基于遗传优化的BP神经网络法在甲烷检测中的应用 Abstract Methaneisanimportantgreenhousegasthatcontributessignificantlytoglobalwarming.Theaccuratedetectionofmethaneintheatmosphereisthereforeessentialformonitoringandcontrollinggreenhousegasemissions.Inthisstudy,abackpropagationneuralnetwork(BPNN)modelwastrainedusinggeneticoptimizationalgorithmtoimproveitspredictionaccuracyofmethaneconcentrationsintheatmosphere.TheresultsshowthatthegeneticoptimizationalgorithmsignificantlyimprovestheperformanceoftheBPNNmodel,andthecombinationofthetwotechniquesprovidesamoreaccurateandreliablepredictionofmethaneconcentrationsintheatmosphere.Theproposedmethodcanbeusedasaneffectivetoolformethanedetectionandmonitoring. Introduction Methaneisakeygreenhousegasthatcontributessignificantlytoglobalwarming.Methaneemissionscomefromvarioussources,suchascoalmines,landfills,livestock,andnaturalgasproduction.Accuratedetectionofmethaneintheatmosphereiscrucialforcontrollinggreenhousegasemissionsanddeterminingthesourcesofmethaneemissions.Traditionalmethodsformethanedetectionincludegaschromatography,infraredabsorption,andmassspectrometry.However,thesemethodsareexpensive,time-consuming,andrequirespecializedequipmentandskilledtechnicians.Amoreefficientandcost-effectiveapproachisthereforeneededtodetectandmonitormethaneconcentrationsintheatmosphere. Artificialneuralnetworkshavebeenwidelyusedforenvironmentalmonitoringandmodelingduetotheirabilitytolearnandadaptthroughtraining.Backpropagationneuralnetworks(BPNNs)areapopulartypeofartificialneuralnetworkthatarewidelyusedinpatternrecognitionandclassificationbecauseoftheirabilitytoaccuratelypredictcomplexpatterns.However,BPNNscanoftengetstuckinlocaloptima,leadingtopoorperformance.Toaddressthis,ageneticoptimizationalgorithmcanbeusedtoenhancetheperformanceoftheBPNNmodelbyfindingtheglobaloptimumofthenetwork. Inthisstudy,weproposeanovelmethodutilizingaBPNNmodeloptimizedwithgeneticalgorithm(GA)toaccuratelypredictmethaneconcentrationsintheatmosphere.Ourproposedmethodovercomesthelimitationsoftrad