| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_a864329f69db5da3ce856121f76fe371 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16Z99-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-048 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F19-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 |
| filingDate |
2017-08-10-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ce0623a849dc48076ba493e766fd1d37
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f4ccbf2d8030a4e7408007e3c6eb7029
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_be9a7f5e5635b475499d2c20d5483fa9 |
| publicationDate |
2017-12-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-107526927-A |
| titleOfInvention |
An Online Robust Soft Sensing Method for Blast Furnace Hot Metal Quality |
| abstract |
The present invention provides an on-line robust soft measurement method for blast furnace molten iron quality, comprising: selecting six controllable variables with the highest correlation to blast furnace molten iron quality parameters among the controllable variables in the blast furnace smelting process as input variables; and simultaneously selecting output variables; Determine the order of the random weight neural network model; initialize the relevant parameters and variables of the random weight neural network; robust initial stage; use the random weight neural network model and the acquired blast furnace ironmaking process data to estimate the current molten iron quality parameters online; Stick online sequential learning phases. In the present invention, the online sequential random weight neural network based on the Cauchy distribution weighted M estimation is introduced, and the contribution of the sample data to the establishment of the model is determined according to the size of the residual, which solves the adverse effects of a large number of outliers on the modeling in the modeling process At the same time, it can constantly correct the model parameters according to the newly measured blast furnace ironmaking process data containing outliers, adapt to the current working conditions, eliminate the influence of outliers and accurately predict. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-108596216-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111444617-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111639801-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110189800-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111639801-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110066895-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110066895-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113628689-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112926266-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-109918702-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112926266-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-109446669-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110189800-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-109446669-B |
| priorityDate |
2017-08-10-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |