slime – SyntaxBug

[CEEMDAN-SMA-LSSVM] Optimizing the least squares support vector machine CEEMDAN-SMA-LSSVM power and wind speed prediction based on CEEMD combined with the slime algorithm with matlab implementation

?About the author: A Matlab simulation developer who loves scientific research. He cultivates his mind and improves his technology simultaneously. For code acquisition, paper reproduction and scientific research simulation cooperation, please send a private message. Personal homepage: Matlab Research Studio Personal credo: Investigate things to gain knowledge. For more complete Matlab code and simulation customization […]

Regression prediction | Optimizing the least squares support vector machine CEEMDAN-SMA-LSSVM power and wind speed prediction based on CEEMD combined with the slime algorithm with matlab implementation

[Wind speed prediction] Optimizing the least squares support vector machine CEEMDAN-SMA-LSSVM power wind speed prediction based on CEEMD combined with the slime algorithm with matlab code

Matlab regression prediction of slime mold optimized bidirectional long short-term memory network (SMA-BILSTM)

?Abouttheauthor:AMatlabsimulationdeveloperwholovesscientificresearch.Hecultivateshismindandimproveshistechnologysimultaneously.ForcooperationonMATLABprojects,pleasesendaprivatemessage. Personalhomepage:MatlabResearchStudio Personalcredo:Investigatethingstogainknowledge. Withtherapiddevelopmentofrenewableenergy,windpowergeneration,asanimportantpartofit,hasreceivedwidespreadattention.However,duetotheuncertaintyandvolatilityofwindpowergeneration,accuratepredictionofwindpowergenerationhasbecomeanimportantissue.Inordertosolvethisproblem,manyresearchershavebeguntoexploretheuseofmachinelearningalgorithmsforwindpowerdataprediction. Amongmachinelearningalgorithms,longshort-termmemorynetwork(LSTM)isacommonlyusedrecurrentneuralnetwork(RNN)modelthatperformswellinprocessingsequencedata.LSTMnetworksareabletocapturelong-termdependenciesinsequencedata,therebyimprovingpredictionaccuracy.However,thetraditionalLSTMmodeloftenhassomeproblemswhenprocessingwindpowerdata,suchasslowmodeltrainingspeedandlowpredictionaccuracy. Inordertosolvetheseproblems,thispaperproposesalongshort-termmemorySMA-biLSTMmodeloptimizedbasedontheSlimeMoldAlgorithm(SMA).Theslimemoldalgorithmisaheuristicalgorithmthatsimulatesthebehaviorofslimemoldsintheprocessofsearchingforfood.Ithasthecharacteristicsofglobalsearchandadaptability.ByapplyingtheslimemoldalgorithmtotheoptimizationprocessoftheLSTMmodel,thetrainingspeedandpredictionaccuracyofthemodelcanbeeffectivelyimproved. Intheexperiment,weusedasetofwindpowerdatasetstotrainandtestthemodel.First,wepreprocessedtheoriginalwindpowerdata,includingdatacleaning,featureextraction,etc.Then,weusedtheslimemoldalgorithmtooptimizetheparametersoftheSMA-biLSTMmodel.Finally,wecomparedtheresultsofwindpowerdatapredictionusingthetraditionalLSTMmodelandtheoptimizedSMA-biLSTMmodel. ExperimentalresultsshowthattheoptimizedSMA-biLSTMmodelshowsobviousadvantagesinwindpowerdataprediction.ComparedwiththetraditionalLSTMmodel,theSMA-biLSTMmodelhashigherpredictionaccuracyandfastertrainingspeed.ThisshowsthattheperformanceoftheLSTMmodelinwindpowerdatapredictioncanbeeffectivelyimprovedbyintroducingtheslimemoldalgorithmforoptimization. Insummary,thispaperproposesalongshort-termmemorySMA-biLSTMmodeloptimizedbasedontheslimemoldalgorithmforwindpowerdataprediction.Experimentalresultsshowthatthemodelhasgoodperformanceinwindpowerdataprediction.Futureresearchcanfurtherexploretheapplicationofotheroptimizationalgorithmstofurtherimprovetheaccuracyandefficiencyofwindpowerdataprediction. Corecode functionhuatu(fitness,process,type) figure plot(fitness) gridon title([type,’Fitnesscurve’]) xlabel(‘Numberofiterations/times’) ylabel(‘Fitnessvalue/MSE’) figure subplot(2,2,1) plot(process(:,1)) gridon xlabel(‘Numberofiterations/times’) ylabel(‘L1/piece’) subplot(2,2,2) plot(process(:,2)) gridon xlabel(‘Numberofiterations/times’) ylabel(‘L2/piece’) subplot(2,2,3) plot(process(:,3)) gridon xlabel(‘Numberofiterations/times’) ylabel(‘K/times’) subplot(2,2,4) plot(process(:,4)) gridon xlabel(‘Numberofiterations/times’) ylabel(‘lr’) subtitle([type,’Hyperparameterschangewiththenumberofiterations’]) ?References [1]LongZhongxiu.Researchandimplementationoflandslidepredictionbasedonsoilslopedataclassificationmodel[D].SouthwestJiaotongUniversity,2020. [2]WangYongsheng.Researchonshort-termwindpoweroutputpowerpredictionbasedondeeplearning[J].[2023-09-08]. [3]WangHaiyue.Researchontheapplicationoffuzzyinferencesystembasedongranularcomputingintimeseriesdataprediction[D].ShandongNormalUniversity,2019. Sometheoriesarequotedfromonlineliterature.Ifthereisanyinfringement,pleasecontactthebloggertodeleteit Followmetoreceivemassivematlabe-booksandmathematicalmodelingmaterials Completeprivatemessagecodeanddataacquisitionandpapersimulationandrealcustomization 1Improvementsandapplicationsofvariousintelligentoptimizationalgorithms Productionscheduling,economicscheduling,assemblylinescheduling,chargingoptimization,workshopscheduling,departureoptimization,reservoirscheduling,three-dimensionalpacking,logisticslocationselection,cargospaceoptimization,busschedulingoptimization,chargingpilelayoutoptimization,workshoplayoutoptimization,Containershipstowageoptimization,waterpumpcombinationoptimization,medicalresourceallocationoptimization,facilitylayoutoptimization,visibleareabasestationanddronesiteselectionoptimization 2Machinelearninganddeeplearning Convolutionalneuralnetwork(CNN),LSTM,supportvectormachine(SVM),leastsquaressupportvectormachine(LSSVM),extremelearningmachine(ELM),kernelextremelearningmachine(KELM),BP,RBF,widthLearning,DBN,RF,RBF,DELM,XGBOOST,TCNrealizewindpowerprediction,photovoltaicprediction,batterylifeprediction,radiationsourceidentification,trafficflowprediction,loadprediction,stockpriceprediction,PM2.5concentrationprediction,batteryhealthstatusprediction,waterbodyOpticalparameterinversion,NLOSsignalidentification,accuratesubwayparkingprediction,transformerfaultdiagnosis 2.Imageprocessing Imagerecognition,imagesegmentation,imagedetection,imagehiding,imageregistration,imagesplicing,imagefusion,imageenhancement,imagecompressedsensing 3Pathplanning Travelingsalesmanproblem(TSP),vehicleroutingproblem(VRP,MVRP,CVRP,VRPTW,etc.),UAVthree-dimensionalpathplanning,UAVcollaboration,UAVformation,robotpathplanning,rastermappathplanning,multimodaltransportationproblems,vehiclecollaborativeUAVpathplanning,antennalineararraydistributionoptimization,workshoplayoutoptimization 4UAVapplication […]

SMA-BP regression prediction | Matlab slime mold optimization algorithm optimizes BP neural network regression prediction

Data classification prediction based on slime mold optimization algorithm SMA optimized BP neural network

Path planning algorithm: robot path planning algorithm based on slime mold optimization – with matlab code

Author’s brief introduction: A Matlab simulation developer who loves scientific research. He cultivates his mind and technology at the same time. Matlab project cooperation can be privately messaged. Personal homepage: Matlab Research Studio Personal creed: Investigate knowledge. For more Matlab simulation content click Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system […]

Tension and compression spring design based on slime mold optimization algorithm-single objective optimization problem

Design of tension and compression spring based on slime mold optimization algorithm-single objective optimization problem 1 Problem description 2. Fitness function 3 Solving the tension-compression spring problem Reference: “Python Intelligent Optimization Algorithm: From Principle to Code Implementation and Application” 1 Description of the problem 2 fitness function ”’Fitness function”’ def fun(X): x1 = X[0] x2 […]

Three-bar Truss Design Based on Slime Mold Algorithm-Monocular Optimization Problem

Three-bar truss design based on slime mold algorithm-monocular optimization problem 1 Problem description 2. Fitness function 3 Solving the three-bar truss problem Reference: “Python Intelligent Optimization Algorithm: From Principle to Code Implementation and Application” 1 Description of the problem 2 fitness function ”’Fitness function”’ def fun(X): x1 = X[0] x2 = X[1] l = 100 […]

Pressure Vessel Design Based on Slime Mold Algorithm-Single Objective Optimization Problem

Pressure vessel design based on slime mold algorithm – single objective optimization problem 1 Problem description 2 Design of fitness function 3 Solve the pressure vessel design problem Reference: “Python Intelligent Optimization Algorithm: From Principle to Code Implementation and Application” 1 Description of the problem 2 Fitness function design ”’Fitness function”’ def fun(X): x1 = […]