Prediction method based on PSO-ADGM-SVR combined model

The invention belongs to the technical field of data mining, and relates to a prediction method based on a PSO-ADGM-SVR combined model, and the method comprises the steps: collecting the nearest slope displacement data, and carrying out the preprocessing of the data; the preprocessed data are input...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: WU QIONGYAO, SU JIAWEI, LIN LONGJUN, WU MINGJUAN, HAN XING, DING XUEPING, LIU YU
Format: Patent
Sprache:chi ; eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator WU QIONGYAO
SU JIAWEI
LIN LONGJUN
WU MINGJUAN
HAN XING
DING XUEPING
LIU YU
description The invention belongs to the technical field of data mining, and relates to a prediction method based on a PSO-ADGM-SVR combined model, and the method comprises the steps: collecting the nearest slope displacement data, and carrying out the preprocessing of the data; the preprocessed data are input into the optimized PSO-ADGM model, and a PSO-ADGM prediction result is obtained; the preprocessed data are input into the optimized PSO-SVR model, and a PSO-SVR prediction result is obtained; calculating a weight vector, and performing weighted combination on the PSO-ADGM prediction result and the PSO-SVR prediction result by using the weight vector to obtain slope displacement predicted by a combined model; the ADGM can explore the trend characteristics of the time series data, the SVR can reflect the randomness characteristics of the time series data, and the ADGM and the SVR have certain complementarity, so that the PSO-ADGM-SVR combined model is obtained by coupling the ADGM model and the SVR model, and the pre
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN118690130A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN118690130A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN118690130A3</originalsourceid><addsrcrecordid>eNrjZDALKEpNyUwuyczPU8hNLcnIT1FISixOTVEA8gOC_XUdXdx9dYPDghSS83OTMvOAErn5Kak5PAysaYk5xam8UJqbQdHNNcTZQze1ID8-tbggMTk1L7Uk3tnP0NDCzNLA0NjA0ZgYNQD0biv3</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Prediction method based on PSO-ADGM-SVR combined model</title><source>esp@cenet</source><creator>WU QIONGYAO ; SU JIAWEI ; LIN LONGJUN ; WU MINGJUAN ; HAN XING ; DING XUEPING ; LIU YU</creator><creatorcontrib>WU QIONGYAO ; SU JIAWEI ; LIN LONGJUN ; WU MINGJUAN ; HAN XING ; DING XUEPING ; LIU YU</creatorcontrib><description>The invention belongs to the technical field of data mining, and relates to a prediction method based on a PSO-ADGM-SVR combined model, and the method comprises the steps: collecting the nearest slope displacement data, and carrying out the preprocessing of the data; the preprocessed data are input into the optimized PSO-ADGM model, and a PSO-ADGM prediction result is obtained; the preprocessed data are input into the optimized PSO-SVR model, and a PSO-SVR prediction result is obtained; calculating a weight vector, and performing weighted combination on the PSO-ADGM prediction result and the PSO-SVR prediction result by using the weight vector to obtain slope displacement predicted by a combined model; the ADGM can explore the trend characteristics of the time series data, the SVR can reflect the randomness characteristics of the time series data, and the ADGM and the SVR have certain complementarity, so that the PSO-ADGM-SVR combined model is obtained by coupling the ADGM model and the SVR model, and the pre</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240924&amp;DB=EPODOC&amp;CC=CN&amp;NR=118690130A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25563,76318</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240924&amp;DB=EPODOC&amp;CC=CN&amp;NR=118690130A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>WU QIONGYAO</creatorcontrib><creatorcontrib>SU JIAWEI</creatorcontrib><creatorcontrib>LIN LONGJUN</creatorcontrib><creatorcontrib>WU MINGJUAN</creatorcontrib><creatorcontrib>HAN XING</creatorcontrib><creatorcontrib>DING XUEPING</creatorcontrib><creatorcontrib>LIU YU</creatorcontrib><title>Prediction method based on PSO-ADGM-SVR combined model</title><description>The invention belongs to the technical field of data mining, and relates to a prediction method based on a PSO-ADGM-SVR combined model, and the method comprises the steps: collecting the nearest slope displacement data, and carrying out the preprocessing of the data; the preprocessed data are input into the optimized PSO-ADGM model, and a PSO-ADGM prediction result is obtained; the preprocessed data are input into the optimized PSO-SVR model, and a PSO-SVR prediction result is obtained; calculating a weight vector, and performing weighted combination on the PSO-ADGM prediction result and the PSO-SVR prediction result by using the weight vector to obtain slope displacement predicted by a combined model; the ADGM can explore the trend characteristics of the time series data, the SVR can reflect the randomness characteristics of the time series data, and the ADGM and the SVR have certain complementarity, so that the PSO-ADGM-SVR combined model is obtained by coupling the ADGM model and the SVR model, and the pre</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZDALKEpNyUwuyczPU8hNLcnIT1FISixOTVEA8gOC_XUdXdx9dYPDghSS83OTMvOAErn5Kak5PAysaYk5xam8UJqbQdHNNcTZQze1ID8-tbggMTk1L7Uk3tnP0NDCzNLA0NjA0ZgYNQD0biv3</recordid><startdate>20240924</startdate><enddate>20240924</enddate><creator>WU QIONGYAO</creator><creator>SU JIAWEI</creator><creator>LIN LONGJUN</creator><creator>WU MINGJUAN</creator><creator>HAN XING</creator><creator>DING XUEPING</creator><creator>LIU YU</creator><scope>EVB</scope></search><sort><creationdate>20240924</creationdate><title>Prediction method based on PSO-ADGM-SVR combined model</title><author>WU QIONGYAO ; SU JIAWEI ; LIN LONGJUN ; WU MINGJUAN ; HAN XING ; DING XUEPING ; LIU YU</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118690130A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>WU QIONGYAO</creatorcontrib><creatorcontrib>SU JIAWEI</creatorcontrib><creatorcontrib>LIN LONGJUN</creatorcontrib><creatorcontrib>WU MINGJUAN</creatorcontrib><creatorcontrib>HAN XING</creatorcontrib><creatorcontrib>DING XUEPING</creatorcontrib><creatorcontrib>LIU YU</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>WU QIONGYAO</au><au>SU JIAWEI</au><au>LIN LONGJUN</au><au>WU MINGJUAN</au><au>HAN XING</au><au>DING XUEPING</au><au>LIU YU</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Prediction method based on PSO-ADGM-SVR combined model</title><date>2024-09-24</date><risdate>2024</risdate><abstract>The invention belongs to the technical field of data mining, and relates to a prediction method based on a PSO-ADGM-SVR combined model, and the method comprises the steps: collecting the nearest slope displacement data, and carrying out the preprocessing of the data; the preprocessed data are input into the optimized PSO-ADGM model, and a PSO-ADGM prediction result is obtained; the preprocessed data are input into the optimized PSO-SVR model, and a PSO-SVR prediction result is obtained; calculating a weight vector, and performing weighted combination on the PSO-ADGM prediction result and the PSO-SVR prediction result by using the weight vector to obtain slope displacement predicted by a combined model; the ADGM can explore the trend characteristics of the time series data, the SVR can reflect the randomness characteristics of the time series data, and the ADGM and the SVR have certain complementarity, so that the PSO-ADGM-SVR combined model is obtained by coupling the ADGM model and the SVR model, and the pre</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN118690130A
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
title Prediction method based on PSO-ADGM-SVR combined model
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T00%3A46%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=WU%20QIONGYAO&rft.date=2024-09-24&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN118690130A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true