IDENTIFICATION OF ON-AND OFF-LINE LINEAR STATE SPACE MODELS USING SUBSPACE METHODS

2015 ◽  
Vol 77 (28) ◽  
Author(s):  
Nurul Syahirah Khalid ◽  
Norhaliza Abd. Wahab ◽  
Muhammad Iqbal Zakaria
Keyword(s):  
State Space ◽  
State Space Model ◽  
Model Identification ◽  
State Space Models ◽  
Subspace Methods ◽  
State Space System ◽  
Linear State ◽  
Value Decomposition ◽  
Error State

In this paper, subspace identification methods are proposed to analyze the differences between On-And Off-Line Linear State Space Models Using Subspace Methods. There are several ways that can estimate the order of the system. For this paper, Singular Value Decomposition (SVD) is used to estimate the order of the system. Comparing with the others methods, this method only need a limited number of input and output data for the determination of the system matrices. Two methods of the subspace algorithm are used which is N4SID (Numerical algorithm for Subspace State Space System Identification) and MOESP (Multivariable Output-Error State-Space model identification).

Recursive Subspace Identification Algorithm using the Propagator Based Method

2017 ◽  
Vol 6 (1) ◽  
pp. 172 ◽  
Author(s):  
Irma Wani Jamaludin Wani Jamaludin ◽  
Norhaliza Abdul Wahab
Keyword(s):  
State Space ◽  
Model Identification ◽  
Industrial Applications ◽  
Dynamic State ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Online Application ◽  
Subspace Algorithms ◽  
State Space System ◽  
Value Decomposition

<p>Subspace model identification (SMI) method is the effective method in identifying dynamic state space linear multivariable systems and it can be obtained directly from the input and output data. Basically, subspace identifications are based on algorithms from numerical algebras which are the QR decomposition and Singular Value Decomposition (SVD). In industrial applications, it is essential to have online recursive subspace algorithms for model identification where the parameters can vary in time. However, because of the SVD computational complexity that involved in the algorithm, the classical SMI algorithms are not suitable for online application. Hence, it is essential to discover the alternative algorithms in order to apply the concept of subspace identification recursively. In this paper, the recursive subspace identification algorithm based on the propagator method which avoids the SVD computation is proposed. The output from Numerical Subspace State Space System Identification (N4SID) and Multivariable Output Error State Space (MOESP) methods are also included in this paper.</p>

scholarly journals Non-linear State-space Model Identification from Video Data using Deep Encoders

2021 ◽  
Vol 54 (7) ◽  
pp. 697-701
Author(s):  
Gerben I. Beintema ◽  
Roland Toth ◽  
Maarten Schoukens
Keyword(s):  
State Space ◽  
State Space Model ◽  
Model Identification ◽  
Video Data ◽  
Space Model ◽  
Non Linear ◽  
Linear State

Numerical Algorithms for Subspace State Space System Identification (N4SID)

1997 ◽  
Author(s):  
Peter Van Overschee ◽  
Bart De Moor ◽  
Wouter Favoreel
Keyword(s):  
State Space ◽  
State Space Model ◽  
Numerical Algorithms ◽  
Numerical Linear Algebra ◽  
Subspace Identification ◽  
Oblique Projection ◽  
Hankel Matrices ◽  
State Space System ◽  
Value Decomposition ◽  
The Given

Abstract We present the basic notions on subspace identification algorithms for linear systems. These methods estimate state sequences or extended observability matrices directly from the given data, through an orthogonal or oblique projection of the row spaces of certain block Hankel matrices into the row spaces of others. The extraction of the state space model is then achieved through the solution of a least squares problem. These algorithms can be elegantly implemented using well-known numerical linear algebra algorithms such as the LQ- and singular value decomposition. The paper aims at giving an overview of the methodologies used in time domain subspace identification. A short overview of frequency domain subspace identification results is also presented.

State-Space Models: From the EM Algorithm to a Gradient Approach

2007 ◽  
Vol 19 (4) ◽  
pp. 1097-1111 ◽  
Author(s):  
Rasmus Kongsgaard Olsson ◽  
Kaare Brandt Petersen ◽  
Tue Lehn-Schiøler
Keyword(s):  
Em Algorithm ◽  
State Space ◽  
Likelihood Function ◽  
State Space Model ◽  
Computation Time ◽  
State Space Models ◽  
High Signal ◽  
The Em Algorithm ◽  
Log Likelihood ◽  
Linear State

Slow convergence is observed in the EM algorithm for linear state-space models. We propose to circumvent the problem by applying any off-the-shelf quasi-Newton-type optimizer, which operates on the gradient of the log-likelihood function. Such an algorithm is a practical alternative due to the fact that the exact gradient of the log-likelihood function can be computed by recycling components of the expectation-maximization (EM) algorithm. We demonstrate the efficiency of the proposed method in three relevant instances of the linear state-space model. In high signal-to-noise ratios, where EM is particularly prone to converge slowly, we show that gradient-based learning results in a sizable reduction of computation time.

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