Name
Abstract | ||
|---|---|---|
In this paper, we present a novel multiple input multiple output (MIMO) linear parameter varying (LPV) state-space refinement system identification algorithm that uses tensor networks. Its novelty mainly lies in representing the LPV sub-Markov parameters, data and state-revealing matrix condensely and in exact manner using specific tensor networks. These representations circumvent the 'curse-of-dimensionality' as they inherit the properties of tensor trains. The proposed algorithm is 'curse-of-dimensionality'-free in memory and computation and has conditioning guarantees. Its performance is illustrated using simulation cases and additionally compared with existing methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1080/00207179.2018.1501515 | INTERNATIONAL JOURNAL OF CONTROL |
Keywords | DocType | Volume |
Identification, LPV systems, tensor trains, subspace methods, closed-loop identification, time-varying systems | Journal | 93 |
Issue | ISSN | Citations |
4 | 0020-7179 | 0 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bilal Gunes | 1 | 8 | 1.50 |
| Jan-Willem van Wingerden | 2 | 154 | 24.73 |
| Michel Verhaegen | 3 | 1074 | 140.85 |