Exact Topology Learning in a Network of Cyclostationary Processes
| Autor: | Saurav Talukdar, Deepjyoti Deka, Harish Doddi, Murti V. Salapaka |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Structure (mathematical logic)
0209 industrial biotechnology Stationary process Cyclostationary process Topology (electrical circuits) 02 engineering and technology Systems and Control (eess.SY) Topology Electrical Engineering and Systems Science - Systems and Control Power (physics) Matrix (mathematics) 020901 industrial engineering & automation Sample size determination FOS: Electrical engineering electronic engineering information engineering |
| Zdroj: | ACC |
| Popis: | Learning the structure of a network from time series data, in particular cyclostationary data, is of significant interest in many disciplines such as power grids, biology and finance. In this article, an algorithm is presented for reconstruction of the topology of a network of cyclostationary processes. To the best of our knowledge, this is the first work to guarantee exact recovery without any assumptions on the underlying structure. The method is based on a lifting technique by which cyclostationary processes are mapped to vector wide sense stationary processes and further on semi-definite properties of matrix Wiener filters for the said processes.We demonstrate the performance of the proposed algorithm on a Resistor-Capacitor network and present the accuracy of reconstruction for varying sample sizes. 6 pages, 3 figures, A version of this paper will appear in American Control Conference 2019 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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