http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-109344995-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_6fbb91e3f8cdf858527814216aa11142 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-23 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q10-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate | 2018-08-29^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b1dd133f7730a5f210f7aefadf16e13d http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_25946cd213e7bb82ea721746979f5aaf |
| publicationDate | 2019-02-15^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-109344995-A |
| titleOfInvention | A multi-step forecasting method for chaotic time series based on density peak clustering |
| abstract | The invention discloses a multi-step prediction method for chaotic time series based on density peak clustering. In the method, adjacent orbits are selected for many times, the phase point boundary is determined by a clustering algorithm, and when the orbit enters the phase point boundary, the phase point boundary is simulated by simulating the phase point boundary. The nearest neighbor orbit is reselected in the form of points, and the adjacent orbit is obtained from the existing one based on the target phase point to multiple times. Combined with the clustering algorithm, the boundary of the phase point is dynamically allocated without artificially determining the cluster center. The influence of human factors improves the problem that the accuracy of multi-step prediction is reduced due to the one-time acquisition of the track by the existing model. Experiments show that the method of the present invention has better generalization performance than the existing multi-step prediction filter model, and is affected by the prediction starting point. has less impact and requires less training set. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110083910-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110414031-A |
| priorityDate | 2018-08-29^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID25572 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID415713197 |
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