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[lssvm regression prediction] Optimizing the least squares support vector machine SO-lssvm based on the snake swarm algorithm to implement data regression prediction with matlab code

The principle and implementation of Debounce and Throttle—preventing an event from being triggered frequently

Original text: http://blog.csdn.net/redtopic/article/details/69396722 When handling events such as resize, scroll, mousemove and keydown/keyup/keypress, usually we do not It is expected that these events will be triggered too frequently, especially if the listener involves a lot of calculations or has very resource-intensive operations. How often? Take mousemove as an example. According to the regulations of DOM […]

Optimizing your Spring Boot application: The secret to preloading

Optimizing your Spring Boot application: the secret of preloading Preloading ApplicationListener implementation built-in events accomplish Custom instance SpringBoot’s CommandLineRunner interface When the bean is loaded and initialized, call Asynchronous tasks Preloading When we have some needs, we need to run a certain method when the project starts, such as a scheduled task. ApplicationListener implementation The […]

[Transfer] [C#] ZIP, RAR compression and decompression

Compressed folder The source code is as follows using System; using System.Data; using System.Configuration; using System.Web; using System.Web.Security; using System.Web.UI; using System.IO; using ICSharpCode.SharpZipLib.Checksums; using ICSharpCode.SharpZipLib.Zip; using ICSharpCode.SharpZipLib.GZip; /// /// Summary description of ZipFloClass /// public class ZipFloClass { public void ZipFile(string strFile, string strZip) { if (strFile[strFile.Length – 1] != Path.DirectorySeparatorChar) strFile + = […]

SpringBoot automatically configures preloaded classes-01

Get configuration class package com.xbm; import org.mybatis.spring.annotation.MapperScan; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; import org.springframework.context.ConfigurableApplicationContext; @MapperScan(“com.xbm”) @SpringBootApplication(scanBasePackages = “com.xbm”) public class SpringBootWebApplication {<!– –> public static void main(String[] args) {<!– –> ConfigurableApplicationContext run = SpringApplication.run(SpringBootWebApplication.class, args); String[] beanDefinitionNames = run.getBeanDefinitionNames(); for (String beanName : beanDefinitionNames) {<!– –> System.out.println(“beanName:” + beanName); } } } operation result: beanName:org.springframework.context.annotation.internalConfigurationAnnotationProcessor beanName:org.springframework.context.annotation.internalAutowiredAnnotationProcessor […]

BO-LSTM regression prediction | Matlab Bayesian algorithm optimizes long short-term memory network regression prediction

?Abouttheauthor:AMatlabsimulationdeveloperwholovesscientificresearch.Hecultivateshismindandimproveshistechnologysimultaneously.ForcooperationonMATLABprojects,pleasesendaprivatemessage. Personalhomepage:MatlabResearchStudio Personalcredo:Investigatethingstogainknowledge. FormorecompleteMatlabcodeandsimulationcustomizationcontent,click IntelligentoptimizationalgorithmNeuralnetworkpredictionRadarcommunicationWirelesssensorPowersystem SignalprocessingImageprocessingPathplanningCellularautomatonDrone Contentintroduction Inthefieldofmachinelearningandartificialintelligence,regressionpredictionoftimeseriesdataisanimportantproblem.Longshort-termmemory(LSTM)isarecurrentneuralnetwork(RNN)architecturewidelyusedtoprocesstimeseriesdata.However,theperformanceoftheLSTMmodelishighlydependentonthechoiceofitshyperparameters,whichmakesthetuningofthemodelverydifficult.Tosolvethisproblem,wecanuseBayesianoptimizationtoautomaticallyfindtheoptimalhyperparameterconfiguration. Bayesianoptimizationisamethodtooptimizetheobjectivefunctionbyselectingsuitablecandidatepointsinthesearchspace.Itestimatestheperformanceoftheobjectivefunctionbybuildingasurrogatemodelandusesthismodeltoguidethesearchprocess.Inthiscase,wecanuseBayesianoptimizationtosearchforahyperparameterconfigurationoftheLSTMmodelthatmaximizestheaccuracyoftheregressionprediction. BO-LSTMisanLSTMmodelbasedonBayesianoptimization,whichcombinestheadvantagesofBayesianoptimizationandLSTMmodels.ThecoreideaofBO-LSTMistoselectthehyperparameterconfigurationoftheLSTMmodelthroughBayesianoptimizationandusetheseconfigurationstotrainandpredicttimeseriesdata.ByiterativelyusingtheBayesianoptimizationalgorithm,BO-LSTMcangraduallyimprovetheperformanceofthemodel,resultinginmoreaccurateregressionpredictions. InordertoimplementtheBO-LSTMmodel,weneedtodefinethehyperparameterspaceandobjectivefunctionoftheLSTMmodel.Thehyperparameterspaceincludesthehiddenlayersize,learningrate,numberofiterations,etc.ofLSTM.Theobjectivefunctioncanbetherootmeansquareerror(RMSE)orthemeanabsoluteerror(MAE)oftheregressionprediction.Bayesianoptimizationalgorithmswillselectcandidatepointsinthehyperparameterspaceanduseanobjectivefunctiontoevaluatetheirperformance.Byiterativelyselectingcandidatepointsandupdatingthesurrogatemodel,Bayesianoptimizationalgorithmscangraduallyimprovetheperformanceofthemodel. TheadvantageofBO-LSTMisthatitcanautomaticallyfindtheoptimalhyperparameterconfigurationwithoutmanualtuning.Thismakesthemodeltrainingandpredictionprocessmoreefficientandaccurate.Inaddition,BO-LSTMcanalsoadapttodifferenttimeseriesdataandachievegoodperformanceondifferentproblems. However,BO-LSTMalsohassomelimitations.First,Bayesianoptimizationalgorithmsarecomputationallyexpensive,especiallywhenthehyperparameterspaceislarge.Secondly,theperformanceofBO-LSTMishighlydependentonthechoiceofobjectivefunctionandthedefinitionofthehyperparameterspace.Therefore,beforeusingBO-LSTM,weneedtocarefullyselecttheobjectivefunctionandhyperparameterspacetoensurethebestresults. Tosumup,thelongshort-termmemoryBO-LSTMmodelbasedonBayesianoptimizationisapowerfultoolforregressionpredictionoftimeseriesdata.Itimprovestheperformanceofthemodelbyautomaticallyselectingtheoptimalhyperparameterconfigurationandachievesgoodresultsinpractice.However,westillneedtopayattentiontothelimitationsofBO-LSTMandmakereasonableadjustmentsandimprovementsaccordingtotheneedsofspecificproblems. Partofthecode %%Example’GpsMultiCorrelator’#3:Generationofenergymatricesresultingfromnon-coherentintegrationswithdifferentperiods % %Generationofenergymatricesresultingfromtheaccumulationofnon-coherentcorrelationresults,overdifferentperiods,between: %-AreceivedsignalincludingaGPSsignal, %-AlocalsignalmatchingintermsofPRN,Dopplerandcodephase. %Parameters SamplingPeriod=100e-9; CarrierFrequency=0; PRN=3; CN0=45*10; Doppler=0; CodePhase=0; Duration=25e-3; %Creationof’GpsSignals’object GPS=… GpsSignals(‘SamplingPeriod’,SamplingPeriod,… ‘CarrierFrequency’,CarrierFrequency,… ‘NextValues’,’replace’,… ‘PRN’,PRN,… ‘CN0’,CN0,… ‘Doppler’,Doppler,… ‘CodePhase’,CodePhase); %Creationof’GpsMultiCorrelator’object MultiCorrelator=… GpsMultiCorrelator(‘SamplingPeriod’,SamplingPeriod,… ‘CarrierFrequency’,CarrierFrequency,… ‘FilterFrequencies’,-4000:500:+4000-500,… ‘CorrelatorCodePhases’,-4:0.5:+4-0.5,… ‘PRN’,PRN,… ‘Doppler’,Doppler,… ‘CodePhase’,CodePhase,… ‘CodePhaseIncrement’,0,… ‘NonCoherentIntegrationPeriod’,5e-3); forn=1:6 %Signalduration/non-coherentintegrationperiod Duration=n*5e-3; %UpdateofGPSsignals GPS.update(‘Duration’,Duration); %Settingofnon-coherentintegrationperiod MultiCorrelator.set(‘NonCoherentIntegrationPeriod’,Duration); […]

C language, convert infix expression to postfix expression and calculate (stack)

1. Convert infix expression to suffix expression and calculate. The suffix expression is in string form. The number is limited to less than 10. Use the number stack operator stack 1 /* Convert infix expression to postfix expression in C language and calculate the result 2 The infix expression contains binary operators and parentheses, and […]

NGO-CNN-SVM classification prediction | Matlab Northern Goshawk algorithm optimizes convolutional neural network-support vector machine classification prediction…

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