http://rdf.ncbi.nlm.nih.gov/pubchem/patent/TW-201145186-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_75b765b53e6651e9863a3a35e4c1e164 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G05B23-0221 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N7-00 |
| filingDate | 2010-06-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e8281207abffaa9c67feec1f93395cc6 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1655c85c04bd4afb4f5306f7e57e4fc0 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0bb112a7983d209c53db9ea991f4c3b5 |
| publicationDate | 2011-12-16-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | TW-201145186-A |
| titleOfInvention | Method for buliding adaptive soft sensor |
| abstract | The invention discloses a method for building adaptive soft sensor. The method comprises the following steps. The input and schedule vectors are constructed, and used to a novel learning algorithm that uses online subtractive clustering to recursively update the structure and parameters of a local model network. Three rules are proposed for updating centers and local model coefficients of existing clusters, for generating new clusters and new models as well as for merging existing clusters and their corresponding models. Once these verified, then the online inferential model can be created to generate the predicted value of process. Thus, it doesn't need much memory space to process the method and easily apply on any other machine. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-104679972-A |
| priorityDate | 2010-06-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 36.