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基于免疫遺傳算法的井下工具試驗裝置模糊神經(jīng)網(wǎng)絡控制的中期報告AbstractThereportpresentstheprogressmadeinthedevelopmentofafuzzyneuralnetworkcontrolsystemforanundergroundtooltestrigusingtheimmunegeneticalgorithm.Themainobjectiveoftheprojectistodevelopacontrolsystemthatcanoptimizetheperformanceoftheundergroundtooltestrig.Thecontrolsystememploysafuzzyneuralnetworkwhichisoptimizedbytheimmunegeneticalgorithm.Theproposedapproachhasshownpromisingresultsintermsofimprovingtheperformanceofthetestrig.Thereportprovidesanoverviewofthetheoreticalbackground,theproposedcontrolsystem,andtheresultsobtainedfrompreliminaryexperiments.IntroductionIntheindustry,undergrounddrillingmachinesarewidelyusedforvariousminingactivitiessuchastheextractionofoil,gas,andminerals.Thesemachinesaremainlyinvolvedinrockdrillingandhaveasignificantimpactontheoverallefficiencyandprofitabilityofminingoperations.Therefore,theperformanceandreliabilityofundergrounddrillingmachinesarecriticalforthesuccessofminingprojects.Testingundergroundtoolsisimportanttoensuretheiroptimalperformance.However,testingmachinesposesachallengeduetotheircomplexityandhighcost.Therefore,developinganefficientcontrolsystemthatcanoptimizetheperformanceofundergrounddrillingmachinesisofsignificantinteresttotheindustry.Inrecentyears,fuzzyneuralnetworkshavebeenwidelyusedincontrolapplicationsduetotheirabilitytohandlecomplexandnonlinearsystems.Fuzzyneuralnetworksemployfuzzylogictomodeluncertainty,andneuralnetworkstolearnandoptimizethecontrolsystem.Immunegeneticalgorithmshavealsobeenusedincontrolapplicationsduetotheirabilitytooptimizecontrolparameters.Therefore,thisprojectproposesthedevelopmentofafuzzyneuralnetworkcontrolsystemoptimizedbytheimmunegeneticalgorithmtocontrolanundergroundtooltestrig.TheoreticalBackgroundFuzzyLogicFuzzylogicisamathematicalrepresentationofuncertaintyandimprecision.Fuzzylogicallowstherepresentationofavariableintermsofmembershipfunctions,whichdeterminethedegreeofmembershipofavariabletoaspecificclass.Fuzzylogichasbeenusedinvariousapplications,suchascontrolsystems,decision-makingsystems,andpatternrecognitionsystems.NeuralNetworksNeuralnetworksareamethodofmachinelearningthathasbeenwidelyusedincontrolapplications.Neuralnetworksconsistofanetworkofinterconnectedneuronsthatcanlearnfromdataandoptimizetheparametersofthecontrolsystem.Thebackpropagationalgorithmiscommonlyusedtotrainneuralnetworks.ImmuneGeneticAlgorithmImmunegeneticalgorithmsareasubclassofgeneticalgorithmsthatareinspiredbythenaturalimmunesystem.Immunegeneticalgorithmsusetheconceptofimmunizationtooptimizetheparametersofthecontrolsystem.Immunegeneticalgorithmshavebeenshowntobeeffectiveinoptimizingcontrolparametersofcomplexandnonlinearsystems.ProposedApproachTheproposedapproachisacontrolsystemthatemploysafuzzyneuralnetworkoptimizedbytheimmunegeneticalgorithm.Thecontrolsystemconsistsofthreemaincomponents:theinputlayer,thehiddenlayer,andtheoutputlayer.Theinputlayerconsistsoftheinputstothecontrolsystem,whicharethedrillingdepth,drillingspeed,drillingpressure,anddrillingtorque.Theinputvaluesaretransformedintofuzzysetsusingmembershipfunctions.Thehiddenlayerconsistsofthefuzzyinferencesystem,whichdeterminesthecontrolactionsbasedontheinputvaluesandthefuzzyrules.ThefuzzyrulesaregeneratedusingtheMamdaniinferencemethod.Theoutputlayerconsistsofthecontrolactions,whicharethedrillingdepth,drillingspeed,drillingpressure,anddrillingtorque.Theoutputvaluesaretransformedfromfuzzysetstocrispvaluesusingdefuzzification.Theimmunegeneticalgorithmisusedtooptimizethefuzzyrulesandtheparametersofthemembershipfunctions.Theimmunegeneticalgorithmemploystheconceptsofimmunizationtooptimizethecontrolparameters.ResultsPreliminaryexperimentshaveshownpromisingresultsinimprovingtheperformanceoftheundergroundtooltestrig.Theoptimizedfuzzyneuralnetworkcontrolsystemachievedahigherdrillingratethantheconventionalcontrolsystem.Theimmunegeneticalgorithmwasalsoshowntobeeffectiveinoptimizingthefuzzyrulesandmembershipfunctions.ConclusionTheproposedapproachofafuzzyneuralnetworkcontrolsystemoptimizedbytheimmunegeneticalgorithmhasshownpromisingresultsinimprovingtheperformanceoftheundergroundtool

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