|Table of Contents|

ExplorationandPracticeofPriceForecastingPlatformforFreshCutFlowersBasedonIntelligentAlgorithm

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2018年20
Page:
191-198
Research Field:
Publishing date:

Info

Title:
ExplorationandPracticeofPriceForecastingPlatformforFreshCutFlowersBasedonIntelligentAlgorithm
Author(s):
QIANYe1; 2; SUNJihong3; YEDan1; 2; PENGLin1; 2; LIWenfeng2
(1.SchoolofBigData,YunnanAgriculturalUniversity,Kunming,Yunnan650201;2.KeyLaboratoryofAgriculturalInformationTechnologyinYunnan,Kunming,Yunnan650201;3.InstituteofScienceandTechnologyinYunnan,Kunming,Yunnan650000)
Keywords:
predict; price; intelligentalgorithm; RBF; GRNN
PACS:
-
DOI:
10.11937/bfyy.20180557
Abstract:
Inordertosolveincompletecollectingdataofallkindsoffreshcutflowers,suchasretailprice,salesvolume,salesrateandsooninthemarket,basedonintelligentalgorithm,apricepredictionplatformoffreshcutflowersisdesignedforretailersandbuyers:thedataoffreshcutflowersarecollectedviathetradingofretailersandbuyers,thenusingthesedataasinputsourcestopredictthepriceoffreshcutflowersbasedonintelligentalgorithm,atlast,theforecastresultsnotonlyarefedbacktofreshcutflowergrowersandplantenterprisestoensuretheireconomicbenefits,butalsocanprovidein-depthresearchdataforresearchers.Inthisstudy,thekeytechniquesoffreshcutflowerpredictionwereexplored.Accordingtothesizeoftheinputlayerdata,RadicalBasicFunction(RBF),GeneralRegressionNeuralNetwork(GRNN)wereappliedrespectivelytobuildthecutflowerpriceforecastingmodel.Takingtherosecutflowersasexample,withthedatapublishedbyDounanFlowerMarketasinputsourceofthetwopredictionmodels.TheexperimentalresultsshowedthatRBFandGRNNweresuitablefordifferentlevelsscaleofinputdatasetsandtheforecastaccuracyrateremainedat85%-95%.

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Last Update: 2018-12-06