Aero-engine load spectrum task section modeling method based on support vector machine regression
The invention discloses an aero-engine load spectrum task segment modeling method based on support vector machine regression. The method comprises the following steps: extracting aero-engine load spectrum task segment data; preprocessing the task segment data, and establishing a training sample set...
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| 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 | SONG YINGDONG YAO XUBO SUN ZHIGANG NIU XUMING LIN XIN JIN YU |
| description | The invention discloses an aero-engine load spectrum task segment modeling method based on support vector machine regression. The method comprises the following steps: extracting aero-engine load spectrum task segment data; preprocessing the task segment data, and establishing a training sample set and a test sample set; inputting a training sample set, initializing parameters of the support vector machine regression model, and setting a parameter change range; obtaining optimal parameters of the support vector machine regression model by adopting a genetic algorithm; training the aero-engine load spectrum task segment simulation model, and calculating a predicted value of a training sample set; inputting a test sample set, and calculating a predicted value of test data by adopting the simulation model; and analyzing the modeling precision of the aero-engine load spectrum task section. The method can solve the problem that accurate and efficient modeling cannot be realized due to various task segments and dis |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN112800541A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN112800541A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN112800541A3</originalsourceid><addsrcrecordid>eNqNjM0KwjAQhHvxIOo7rA9QaP0Br6Uonjx5L2sztsEmG7Kpz28EH8DT8DHfzLLgBlFK-MF60CRsSAP6FGdHifVFmsGKJycGk_UDOaRRDD1YYSgXOocgMdE7ixLJcT9-ryKGCNU8XReLJ0-KzS9XxfZyvrfXEkE6aOAeHqlrb3W9O1XV8VA3-3-cDzGcPgs</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Aero-engine load spectrum task section modeling method based on support vector machine regression</title><source>esp@cenet</source><creator>SONG YINGDONG ; YAO XUBO ; SUN ZHIGANG ; NIU XUMING ; LIN XIN ; JIN YU</creator><creatorcontrib>SONG YINGDONG ; YAO XUBO ; SUN ZHIGANG ; NIU XUMING ; LIN XIN ; JIN YU</creatorcontrib><description>The invention discloses an aero-engine load spectrum task segment modeling method based on support vector machine regression. The method comprises the following steps: extracting aero-engine load spectrum task segment data; preprocessing the task segment data, and establishing a training sample set and a test sample set; inputting a training sample set, initializing parameters of the support vector machine regression model, and setting a parameter change range; obtaining optimal parameters of the support vector machine regression model by adopting a genetic algorithm; training the aero-engine load spectrum task segment simulation model, and calculating a predicted value of a training sample set; inputting a test sample set, and calculating a predicted value of test data by adopting the simulation model; and analyzing the modeling precision of the aero-engine load spectrum task section. The method can solve the problem that accurate and efficient modeling cannot be realized due to various task segments and dis</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</subject><creationdate>2021</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&date=20210514&DB=EPODOC&CC=CN&NR=112800541A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210514&DB=EPODOC&CC=CN&NR=112800541A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SONG YINGDONG</creatorcontrib><creatorcontrib>YAO XUBO</creatorcontrib><creatorcontrib>SUN ZHIGANG</creatorcontrib><creatorcontrib>NIU XUMING</creatorcontrib><creatorcontrib>LIN XIN</creatorcontrib><creatorcontrib>JIN YU</creatorcontrib><title>Aero-engine load spectrum task section modeling method based on support vector machine regression</title><description>The invention discloses an aero-engine load spectrum task segment modeling method based on support vector machine regression. The method comprises the following steps: extracting aero-engine load spectrum task segment data; preprocessing the task segment data, and establishing a training sample set and a test sample set; inputting a training sample set, initializing parameters of the support vector machine regression model, and setting a parameter change range; obtaining optimal parameters of the support vector machine regression model by adopting a genetic algorithm; training the aero-engine load spectrum task segment simulation model, and calculating a predicted value of a training sample set; inputting a test sample set, and calculating a predicted value of test data by adopting the simulation model; and analyzing the modeling precision of the aero-engine load spectrum task section. The method can solve the problem that accurate and efficient modeling cannot be realized due to various task segments and dis</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>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2021</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjM0KwjAQhHvxIOo7rA9QaP0Br6Uonjx5L2sztsEmG7Kpz28EH8DT8DHfzLLgBlFK-MF60CRsSAP6FGdHifVFmsGKJycGk_UDOaRRDD1YYSgXOocgMdE7ixLJcT9-ryKGCNU8XReLJ0-KzS9XxfZyvrfXEkE6aOAeHqlrb3W9O1XV8VA3-3-cDzGcPgs</recordid><startdate>20210514</startdate><enddate>20210514</enddate><creator>SONG YINGDONG</creator><creator>YAO XUBO</creator><creator>SUN ZHIGANG</creator><creator>NIU XUMING</creator><creator>LIN XIN</creator><creator>JIN YU</creator><scope>EVB</scope></search><sort><creationdate>20210514</creationdate><title>Aero-engine load spectrum task section modeling method based on support vector machine regression</title><author>SONG YINGDONG ; YAO XUBO ; SUN ZHIGANG ; NIU XUMING ; LIN XIN ; JIN YU</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN112800541A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2021</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>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><toplevel>online_resources</toplevel><creatorcontrib>SONG YINGDONG</creatorcontrib><creatorcontrib>YAO XUBO</creatorcontrib><creatorcontrib>SUN ZHIGANG</creatorcontrib><creatorcontrib>NIU XUMING</creatorcontrib><creatorcontrib>LIN XIN</creatorcontrib><creatorcontrib>JIN YU</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>SONG YINGDONG</au><au>YAO XUBO</au><au>SUN ZHIGANG</au><au>NIU XUMING</au><au>LIN XIN</au><au>JIN YU</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Aero-engine load spectrum task section modeling method based on support vector machine regression</title><date>2021-05-14</date><risdate>2021</risdate><abstract>The invention discloses an aero-engine load spectrum task segment modeling method based on support vector machine regression. The method comprises the following steps: extracting aero-engine load spectrum task segment data; preprocessing the task segment data, and establishing a training sample set and a test sample set; inputting a training sample set, initializing parameters of the support vector machine regression model, and setting a parameter change range; obtaining optimal parameters of the support vector machine regression model by adopting a genetic algorithm; training the aero-engine load spectrum task segment simulation model, and calculating a predicted value of a training sample set; inputting a test sample set, and calculating a predicted value of test data by adopting the simulation model; and analyzing the modeling precision of the aero-engine load spectrum task section. The method can solve the problem that accurate and efficient modeling cannot be realized due to various task segments and dis</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN112800541A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
| title | Aero-engine load spectrum task section modeling method based on support vector machine regression |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T02%3A32%3A44IST&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=SONG%20YINGDONG&rft.date=2021-05-14&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN112800541A%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 |