Name
Abstract | ||
|---|---|---|
This article first systematically classified the uncertainty and provided the multi-scale fractional ordered association model in accordance with the multiple uncertainty time series. From the mathematical point of view, the model used in this thesis extended the integer-order correlation measurement to the fractional-order correlation measurement; elongate the information recognition from point to line, and rolled out the non-process identification to the process identification from the identification point of view. Introduced the multi-scale interaction identification method through the imitation of human beings' process identification, and achieved the accurate identification form coarse to fine. Example shows that, fractional-order association algorithm can provide much more related information comparing with the integer-order one; the import of the multi-scale interactive iteration greatly enhanced the intelligent of the model and the correlative accuracy. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.4304/jcp.7.11.2617-2622 | JOURNAL OF COMPUTERS |
Keywords | Field | DocType |
fractional order, multi-scale, uncertainty, time series, association | Correlative,Computer science,Correlation,Artificial intelligence,Imitation,Process identification,Machine learning | Journal |
Volume | Issue | ISSN |
7 | 11 | 1796-203X |
Citations | PageRank | References |
1 | 0.37 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuran Liu | 1 | 2 | 1.87 |
| Mingliang Hou | 2 | 5 | 3.35 |
| Yanglie Fu | 3 | 1 | 0.37 |