Print Email Facebook Twitter Tensor networks for MIMO LPV system identification Title Tensor networks for MIMO LPV system identification Author Gunes, Bilal (TU Delft Team Jan-Willem van Wingerden) van Wingerden, J.W. (TU Delft Team Jan-Willem van Wingerden) Verhaegen, M.H.G. (TU Delft Team Raf Van de Plas) Date 2018 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. Subject closed-loop identificationIdentificationLPV systemssubspace methodstensor trainstime-varying systems To reference this document use: http://resolver.tudelft.nl/uuid:9b81f383-1c36-4c9d-a91b-d28e32ff2625 DOI https://doi.org/10.1080/00207179.2018.1501515 ISSN 0020-7179 Source International Journal of Control, 93 (2020) (4), 797-811 Part of collection Institutional Repository Document type journal article Rights © 2018 Bilal Gunes, J.W. van Wingerden, M.H.G. Verhaegen Files PDF Tensor_networks_for_MIMO_ ... cation.pdf 2.08 MB Close viewer /islandora/object/uuid:9b81f383-1c36-4c9d-a91b-d28e32ff2625/datastream/OBJ/view