http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-104951803-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2411 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate | 2015-06-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2018-03-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2018-03-13-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-104951803-B |
| titleOfInvention | Soft sensor method for aviation fuel dry point in atmospheric distillation column based on dynamic moving window least squares support vector machine |
| abstract | The invention discloses a soft measurement method for aviation coal dry point of atmospheric distillation tower based on dynamic moving window least squares support vector machine. The method disclosed in the invention selects relevant operation and state parameters of atmospheric distillation tower as the input of the model , the dry point of jet fuel to be predicted is taken as the output of the model, the historical operation data of the rectification tower is selected as the initial training sample, and the initial model of dry point of jet fuel is established by using the least squares support vector machine method. In addition, based on the analysis of the time-varying characteristics of the atmospheric distillation column, a dynamic moving window-based update strategy of sample deletion and sample addition is proposed, and two modes of sample deletion and sample addition are used to implement incrementally Parameter solving and model updating. |
| priorityDate | 2015-06-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 14.