Cut tobacco drying process parameter selection method based on particle swarm optimization neural network
The invention discloses a cut tobacco drying process parameter selection method based on a particle swarm optimization neural network, and the method comprises the following steps: collecting cut tobacco drying process parameters to construct sample data, each sample comprising a group of cut tobacc...
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 | YE ZHIHUI XU YUANGEN SHI DINGKE WANG LIUJING QIAN JIE |
| description | The invention discloses a cut tobacco drying process parameter selection method based on a particle swarm optimization neural network, and the method comprises the following steps: collecting cut tobacco drying process parameters to construct sample data, each sample comprising a group of cut tobacco drying process parameters and a cut tobacco quality probability corresponding to the group of process parameters; constructing a neural network, optimizing network parameters of the neural network by utilizing sample data, during optimization, initializing the network parameters into particle individuals, taking prediction errors of samples as individual fitness, optimizing the network parameters by adopting a particle swarm optimization algorithm, and taking the neural network with determined parameters as a process parameter selection model; and selecting cut tobacco drying process parameters by using the process parameter selection model. The method can quickly and accurately select the process parameters capa |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN113673679A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN113673679A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN113673679A3</originalsourceid><addsrcrecordid>eNqNykEKwkAMheFuXIh6h3gAF6WguJSiuHLlvqQzUQdnmiFJKXp6B_EAwoOfB9-8Cu1oYNyjcwxeXmG4QxZ2pAoZBRMZCShFchZ4gPIf7KFHJQ_lF2PBRQKdUBJwtpDCG792oFEwltjE8lxWsxtGpdWvi2p9Ol7b84Yyd6QZHRXZtZe6bra7sv2h-cd8ALiAQbM</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Cut tobacco drying process parameter selection method based on particle swarm optimization neural network</title><source>esp@cenet</source><creator>YE ZHIHUI ; XU YUANGEN ; SHI DINGKE ; WANG LIUJING ; QIAN JIE</creator><creatorcontrib>YE ZHIHUI ; XU YUANGEN ; SHI DINGKE ; WANG LIUJING ; QIAN JIE</creatorcontrib><description>The invention discloses a cut tobacco drying process parameter selection method based on a particle swarm optimization neural network, and the method comprises the following steps: collecting cut tobacco drying process parameters to construct sample data, each sample comprising a group of cut tobacco drying process parameters and a cut tobacco quality probability corresponding to the group of process parameters; constructing a neural network, optimizing network parameters of the neural network by utilizing sample data, during optimization, initializing the network parameters into particle individuals, taking prediction errors of samples as individual fitness, optimizing the network parameters by adopting a particle swarm optimization algorithm, and taking the neural network with determined parameters as a process parameter selection model; and selecting cut tobacco drying process parameters by using the process parameter selection model. The method can quickly and accurately select the process parameters capa</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</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=20211119&DB=EPODOC&CC=CN&NR=113673679A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20211119&DB=EPODOC&CC=CN&NR=113673679A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>YE ZHIHUI</creatorcontrib><creatorcontrib>XU YUANGEN</creatorcontrib><creatorcontrib>SHI DINGKE</creatorcontrib><creatorcontrib>WANG LIUJING</creatorcontrib><creatorcontrib>QIAN JIE</creatorcontrib><title>Cut tobacco drying process parameter selection method based on particle swarm optimization neural network</title><description>The invention discloses a cut tobacco drying process parameter selection method based on a particle swarm optimization neural network, and the method comprises the following steps: collecting cut tobacco drying process parameters to construct sample data, each sample comprising a group of cut tobacco drying process parameters and a cut tobacco quality probability corresponding to the group of process parameters; constructing a neural network, optimizing network parameters of the neural network by utilizing sample data, during optimization, initializing the network parameters into particle individuals, taking prediction errors of samples as individual fitness, optimizing the network parameters by adopting a particle swarm optimization algorithm, and taking the neural network with determined parameters as a process parameter selection model; and selecting cut tobacco drying process parameters by using the process parameter selection model. The method can quickly and accurately select the process parameters capa</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>PHYSICS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2021</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNykEKwkAMheFuXIh6h3gAF6WguJSiuHLlvqQzUQdnmiFJKXp6B_EAwoOfB9-8Cu1oYNyjcwxeXmG4QxZ2pAoZBRMZCShFchZ4gPIf7KFHJQ_lF2PBRQKdUBJwtpDCG792oFEwltjE8lxWsxtGpdWvi2p9Ol7b84Yyd6QZHRXZtZe6bra7sv2h-cd8ALiAQbM</recordid><startdate>20211119</startdate><enddate>20211119</enddate><creator>YE ZHIHUI</creator><creator>XU YUANGEN</creator><creator>SHI DINGKE</creator><creator>WANG LIUJING</creator><creator>QIAN JIE</creator><scope>EVB</scope></search><sort><creationdate>20211119</creationdate><title>Cut tobacco drying process parameter selection method based on particle swarm optimization neural network</title><author>YE ZHIHUI ; XU YUANGEN ; SHI DINGKE ; WANG LIUJING ; QIAN JIE</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN113673679A3</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>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>PHYSICS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>YE ZHIHUI</creatorcontrib><creatorcontrib>XU YUANGEN</creatorcontrib><creatorcontrib>SHI DINGKE</creatorcontrib><creatorcontrib>WANG LIUJING</creatorcontrib><creatorcontrib>QIAN JIE</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>YE ZHIHUI</au><au>XU YUANGEN</au><au>SHI DINGKE</au><au>WANG LIUJING</au><au>QIAN JIE</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Cut tobacco drying process parameter selection method based on particle swarm optimization neural network</title><date>2021-11-19</date><risdate>2021</risdate><abstract>The invention discloses a cut tobacco drying process parameter selection method based on a particle swarm optimization neural network, and the method comprises the following steps: collecting cut tobacco drying process parameters to construct sample data, each sample comprising a group of cut tobacco drying process parameters and a cut tobacco quality probability corresponding to the group of process parameters; constructing a neural network, optimizing network parameters of the neural network by utilizing sample data, during optimization, initializing the network parameters into particle individuals, taking prediction errors of samples as individual fitness, optimizing the network parameters by adopting a particle swarm optimization algorithm, and taking the neural network with determined parameters as a process parameter selection model; and selecting cut tobacco drying process parameters by using the process parameter selection model. The method can quickly and accurately select the process parameters capa</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN113673679A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Cut tobacco drying process parameter selection method based on particle swarm optimization neural network |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T09%3A34%3A40IST&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=YE%20ZHIHUI&rft.date=2021-11-19&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN113673679A%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 |