Source:http://linkedlifedata.com/resource/pubmed/id/12935220
Switch to
| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
1 Pt 2
|
| pubmed:dateCreated |
2003-8-25
|
| pubmed:abstractText |
The problem of identifying continuous spatiotemporal nonlinear systems from noisy and indirect observations is determined by its computational complexity. We propose a solution by means of nonlinear state space filtering along with a state partition technique. The method is demonstrated to be computationally feasible for spatiotemporal data with properties that occur typically in experimental recordings. It is applied to one component of the simulated chaotic data of a two-component reaction diffusion system, yielding estimates of both the unobserved state component and the diffusion constant.
|
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:status |
PubMed-not-MEDLINE
|
| pubmed:month |
Jul
|
| pubmed:issn |
1539-3755
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:volume |
68
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
016202
|
| pubmed:year |
2003
|
| pubmed:articleTitle |
Identification of nonlinear spatiotemporal systems via partitioned filtering.
|
| pubmed:affiliation |
Center for Dynamics of Complex Systems, University of Potsdam, 14469 Potsdam, Germany.
|
| pubmed:publicationType |
Journal Article
|