Dendrobium candidum growth environment prediction method
The present invention relates to a dendrobium candidum growth environment prediction method. The method comprises the steps of a growth environment prediction model and an improved BP neural network.The growth environment prediction model comprises normalizing the obtained parameters, so that the ne...
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| creator | TU HANGYAO XIE FANFAN DING JINTING |
| description | The present invention relates to a dendrobium candidum growth environment prediction method. The method comprises the steps of a growth environment prediction model and an improved BP neural network.The growth environment prediction model comprises normalizing the obtained parameters, so that the neural network has good fitting effect on the samples; and the improved BP neural network comprises that a primary fuzzy controller processes an input error signal e(n) and a secondary fuzzy controller processes the input error change rate (as shown in the specification). The beneficial effects of the method provided by the present invention are that: the present invention provides an improved BP neural network algorithm for plant growth environment prediction, and the improved BP neural networkovercomes the defect of a slow convergence rate of the traditional BP neural network and changes the step length, so that the adaptive variable step length is implemented by using the fuzzy control according to the growth envi |
| format | Patent |
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The method comprises the steps of a growth environment prediction model and an improved BP neural network.The growth environment prediction model comprises normalizing the obtained parameters, so that the neural network has good fitting effect on the samples; and the improved BP neural network comprises that a primary fuzzy controller processes an input error signal e(n) and a secondary fuzzy controller processes the input error change rate (as shown in the specification). The beneficial effects of the method provided by the present invention are that: the present invention provides an improved BP neural network algorithm for plant growth environment prediction, and the improved BP neural networkovercomes the defect of a slow convergence rate of the traditional BP neural network and changes the step length, so that the adaptive variable step length is implemented by using the fuzzy control according to the growth envi</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>2018</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=20180608&DB=EPODOC&CC=CN&NR=108133282A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25562,76317</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20180608&DB=EPODOC&CC=CN&NR=108133282A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>TU HANGYAO</creatorcontrib><creatorcontrib>XIE FANFAN</creatorcontrib><creatorcontrib>DING JINTING</creatorcontrib><title>Dendrobium candidum growth environment prediction method</title><description>The present invention relates to a dendrobium candidum growth environment prediction method. The method comprises the steps of a growth environment prediction model and an improved BP neural network.The growth environment prediction model comprises normalizing the obtained parameters, so that the neural network has good fitting effect on the samples; and the improved BP neural network comprises that a primary fuzzy controller processes an input error signal e(n) and a secondary fuzzy controller processes the input error change rate (as shown in the specification). The beneficial effects of the method provided by the present invention are that: the present invention provides an improved BP neural network algorithm for plant growth environment prediction, and the improved BP neural networkovercomes the defect of a slow convergence rate of the traditional BP neural network and changes the step length, so that the adaptive variable step length is implemented by using the fuzzy control according to the growth envi</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>2018</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLBwSc1LKcpPyizNVUhOzEvJTAEy0ovyy0syFFLzyjKL8vNyU_NKFAqKUlMyk0sy8_MUclNLMvJTeBhY0xJzilN5oTQ3g6Kba4izh25qQX58anFBYnJqXmpJvLOfoYGFobGxkYWRozExagBfri8H</recordid><startdate>20180608</startdate><enddate>20180608</enddate><creator>TU HANGYAO</creator><creator>XIE FANFAN</creator><creator>DING JINTING</creator><scope>EVB</scope></search><sort><creationdate>20180608</creationdate><title>Dendrobium candidum growth environment prediction method</title><author>TU HANGYAO ; XIE FANFAN ; DING JINTING</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN108133282A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2018</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>TU HANGYAO</creatorcontrib><creatorcontrib>XIE FANFAN</creatorcontrib><creatorcontrib>DING JINTING</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>TU HANGYAO</au><au>XIE FANFAN</au><au>DING JINTING</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Dendrobium candidum growth environment prediction method</title><date>2018-06-08</date><risdate>2018</risdate><abstract>The present invention relates to a dendrobium candidum growth environment prediction method. The method comprises the steps of a growth environment prediction model and an improved BP neural network.The growth environment prediction model comprises normalizing the obtained parameters, so that the neural network has good fitting effect on the samples; and the improved BP neural network comprises that a primary fuzzy controller processes an input error signal e(n) and a secondary fuzzy controller processes the input error change rate (as shown in the specification). The beneficial effects of the method provided by the present invention are that: the present invention provides an improved BP neural network algorithm for plant growth environment prediction, and the improved BP neural networkovercomes the defect of a slow convergence rate of the traditional BP neural network and changes the step length, so that the adaptive variable step length is implemented by using the fuzzy control according to the growth envi</abstract><oa>free_for_read</oa></addata></record> |
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| 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 | Dendrobium candidum growth environment prediction method |
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