“Determining whether a point is in a polygon”, I thought it was a very difficult problem at first, but after searching on Google, I found that it is actually quite simple. The algorithm used is called “Ray-casting Algorithm”, which in Chinese should be called “Ray Casting Algorithm” , here is the Wikipedia description: [Wikipedia] Simply […]
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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); […]
uni-app – Mobile terminal (iOS & Android) batch upload files, supports retransmission, deletion, multiple selection, carries parameters, and progress monitoring
About the author: Programmer Banxia, a full-stack programmer, is good at using various programming languages and frameworks, such as JavaScript, React, Node.js, Java, Python, Django, MySQL, etc. Focus on large-scale applications I share hard-core information about front-end and back-end, and I am also a UP owner who updates as needed. You can find me on […]
Algorithm for javascript two-dimensional array (4) filtering and grouping to determine whether there is automatic key value supplementation
Algorithm review After converting JSON to an array, filter the groups and output, and determine whether the key value exists and add 0 Problem description 1. There are four projects, the array is: var projectData = [’60 meters’, ‘3000 meters’, ‘load’, ‘100 meters’]; 2. There are four team members, and the array is: var NameData […]
Qt/C++ TCP-based communication (dual-terminal, multi-terminal, file)
Necessary introduction To use the TCP communication function in Qt, you need to load the network module in qmake: QT + =network The base classes of QTcpServer and QTcpSocket both inherit from QObject, which means that when we customize components, we can extend various functions of the UI library based on them as base classes, […]
The key to MIP’s accurate algorithm-determining the bounds
Table of Contents 1. What is the boundary? 2. How to update upper and lower bounds 2.1 A series of algorithms based on branch and bound 2.2 Decomposition of benders MIP precise algorithms include branch and bound, branch cutting, branch pricing, benders decomposition, etc. The former is a type of algorithm based on branch and […]
In simple terms, SpringBoot integrates Quartz to implement scheduled tasks and Redis health detection (1)
Table of Contents Preface Environment configuration Quartz What is Quartz? Application scenarios core components Job JobDetail Trigger CronTrigger SimpleTrigger Scheduler task storage RAM JDBC Import dependencies scheduled tasks sales statistics Redis detection use Precautions Foreword The Quartz framework was introduced in Yuexiang Campus 1.0 to implement the daily sales statistics function for merchants. The project […]
Android Termux builds a Web server [public network remote mobile phone Android server]
Overview Termux is an Android terminal emulation application that is used to build a complete Linux environment on an Android phone. It can implement many basic operations under Linux. Termux can run normally without root permissions. Apache is an open source web server software that is widely used due to its cross-platform and security and […]
Data Analysis-05-Determine key behaviors + score channel quality (including code and data)
Article directory 0. Get the data set 1. Project goals a. Short-term channel rating system b. Determination of key behaviors 2. Data reading 3. Indicator screening 4. Weight scoring 4.1. Consistency test 5. Results presentation 5.1. Judgment Dimension 0. Obtain data set Follow the public account: “AI Learning Planet“ Reply: Determine key behaviors to score […]
Android Jetpack Compose determines the scope of reorganization and optimizes reorganization
1. Overview Compose’s reorganization is intelligent. When reorganizing, the Composable function will try its best to skip unnecessary reorganization and only reorganize the UI that needs to be changed. So how does Compose decide that the UI needs to change? Or in other words, how does Compose determine the scope of reorganization. If reorganization occurs […]