http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CA-2057078-C
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N7-046 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F8-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-043 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F9-44 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N7-04 |
| filingDate | 1991-03-12-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2000-04-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_31733b141423b310b88fc097d7ebbf2e http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b1940c4c362d58e5c3461bb9be87ed66 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_bea537f98b944a630f0ddbe05f5a943b http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1441f7eb2df5226d8575881a8312c10d http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6f1c8380e37fd2b90ef34ba86860954b http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0128c1350cf8e41cffc82fa398c78f60 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_de379f65955cd0bffc8eeb29ff48c6ce |
| publicationDate | 2000-04-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CA-2057078-C |
| titleOfInvention | Neuro-fuzzy fusion data processing system |
| abstract | The present invention relates to a data processing system in a hierarchical network configuration for executing applicable data processes in a comprehensible and executable form. An object of the present invention is to allow, data processing capabilities to be established with high precision in a short time based on a fuzzy-neuro-integrated concept. A fuzzy model is generated by a data processing system in the form of membership functions and fuzzy rules as technical information relating to a control target. According to this fuzzy model, a weight value of the connection between neurons is set and a pre-wired neural network is established. Then, the data of the control target are learned by the neural network. The connection state and a weight value of the neural network after the learning enable tuning of the fuzzy model. |
| priorityDate | 1990-03-12-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 34.