http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022398366-A1
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_4ddcb273a108a5d8472b335280098e06 |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F2113-04 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-27 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G05B17-02 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F30-27 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G05B17-02 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00 |
| filingDate | 2019-09-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_de76f9417aabbec2772016e873f2589f http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_dbcad84e5893244af1c290a219866d5b http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e274a07c2fe0230508db8f7b44f04fe3 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_728c3f3a41bd480c95c9466f3fbf3ea6 |
| publicationDate | 2022-12-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | US-2022398366-A1 |
| titleOfInvention | Nonlinear Model Modeling Method, Device and Storage Medium |
| abstract | Various embodiments of the teachings herein include a nonlinear model modeling method. The method may include: determining complete design point data for each of multiple target nonlinear underlying process of multiple types of equipment; establishing a descriptive formula of the process with the ratio of a similarity parameter supported by a similarity criterion to a similarity parameter based on design point data, to obtain a universal model of the process; constructing a machine learning algorithm between the parameter of the actual working condition and the variable parameter and establishing a correlation between the machine learning algorithm and the universal model; and taking the universal models of all the target nonlinear underlying processes of each type of equipment and the correlated machine learning algorithms as a universal model of the type of equipment. The universal model comprises a variable parameter that changes as a parameter of an actual working condition changes. |
| priorityDate | 2019-09-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 25.