http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-106862284-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F2218-08 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/B21B37-28 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/B21B38-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/B21B38-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-02 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-126 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-02 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/B21B38-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/B21B37-28 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/B21B38-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-12 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-00 |
| filingDate | 2017-03-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2018-09-04-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2018-09-04-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-106862284-B |
| titleOfInvention | A method for signal pattern recognition of cold-rolled strip |
| abstract | A method for signal pattern recognition of cold-rolled strips, the method comprising the following steps: collecting the shape measurement values of each measurement section in the width direction of the cold-rolled strip steel measured online by a shapemeter, and obtaining the shape value of each measurement section; The original data output by the shape meter is input to an n-layer neural network as the feature extraction layer, mainly through training to let the network automatically extract features to eliminate artificial traces; use the improved quantum neural network based on genetic algorithm for plate shape recognition. The invention applies the improved quantum neural network of the multi-layer excitation function optimized by the genetic algorithm to the shape pattern recognition technology, which significantly improves the training efficiency of the network and effectively solves the problems of accuracy and real-time performance encountered in the traditional shape recognition method It is not ideal enough, the network structure is complex, the training time is long, and the stability and robustness are poor. |
| priorityDate | 2017-03-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID451080591 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID115305 |
Total number of triples: 22.