Name
Abstract | ||
|---|---|---|
The problem considered here is to represent a stationary stochastic processy with a low-dimensional stochastic model. This problem occurs when the state space of an exact realization ofy has a very large dimension. The reduction is obtained in this large state space, exploiting its markovian structure to characterize
all markovian subspaces, among which a reducedk-dimensional model is sought. The concept of markovian basis is introduced, and its equivalence with the Malmquist basis in
the spectral domain is shown. An algorithm with polynomial complexity to compute an approximate model is given. |
Year | DOI | Venue |
|---|---|---|
1991 | 10.1007/BF02551265 | MCSS |
Keywords | Field | DocType |
Model reduction, Stochastic realization, L2-approximation, Restricted shift | Mathematical optimization,Markov process,Linear subspace,Equivalence (measure theory),Stochastic modelling,Polynomial complexity,Realization (systems),State space,Mathematics | Journal |
Volume | Issue | ISSN |
4 | 2 | 1435-568X |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Gombani | 1 | 8 | 8.11 |