http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-108845072-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-049 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N31-005 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N31-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 |
| filingDate | 2018-07-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2020-12-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2020-12-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-108845072-B |
| titleOfInvention | 4-CBA content dynamic soft measurement method based on convolutional neural network |
| abstract | The invention discloses a convolutional neural network-based dynamic soft measurement method for 4-CBA content, which is used for calculating the 4-CBA content generated in a PTA oxidation process, the method firstly constructs a mapping relation between the input and the output of a dynamic soft measurement model based on a convolutional neural network, takes a time sequence data block of a relevant measurable variable in the PTA oxidation process as the input of the dynamic soft measurement model, and takes the 4-CBA as the output; inputting a time sequence data block into a convolutional neural network in which a convolutional layer and a pooling layer are alternately distributed, wherein the convolutional layer and the pooling layer are both 2 layers, the first layer of pooling adopts one-dimensional maximum pooling to extract features after convolution, the second layer of pooling adopts maximum pooling with the same size as a feature map output by the convolutional layer for sampling, the output of the last layer of pooling is calculated by using a linear function to obtain an output result, and the result is compared with 4-CBA analysis data and parameters are updated; the dynamic soft measurement model is simple and easy to realize, and the measurement precision of the model is improved. |
| priorityDate | 2018-07-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 18.