Parallel QR Implementation of Subspace Identification with Parsimonious Models

Fast Implementation of the Qr Factorization in Subspace Identification

Two recent approaches [4, 14] in subspace identification problems require the computation of the R factor of the QR factorization of a block–Hankel matrix H , which, in general has a huge number of rows. Since the data are perturbed by noise, the involved matrix H is, in general, full rank. It is well known that, from a theoretical point of view, the R factor of the QR factorization of H is equ...

Parallel implementation of linear repetitive processes identification using subspace algorithms

Abstract—This paper presents a new parallel approach to identification of linear repetitive processes based on subspace algorithms. Parallel realizations of these algorithms are tested on various graphic cards that use NVIDIA CUDA technology. The paper describes implementation of subspace identification algorithms and their parallel speedup, efficiency, throughput, and delay. The parallel appro...

A parallel implementation of the QR-algorithm

In this paper a parallel implementation of the QR algorithm for the eigenvalues of a non-Hermitian matrix is proposed. The algorithm is designed to run eeciently on a linear array of processors that communicate by accessing their neighbors' memory. A module for building such arrays, the Maryland crab, is also described.

Closed-Loop PARSIMonious Subspace Identification: Theory and Application to MPC

We propose in this paper a novel subspace identification method, based on PARSIMonious parameterization (Qin et al., 2005), and we show that such algorithm guarantees consistent estimates of the Markov parameters with open-loop and closed-loop data. The method uses the predictor form and it effectively exploits in all steps the Toeplitz structure of the Markov parameters’ matrices. After evalua...

Efficient implementation of Subspace method identification Algorithms