Journal ArticleDOI
Recursive subspace identification of linear and non-linear Wiener state-space models
TLDR
The MOESP class of identification algorithms are made recursive on the basis of various updating schemes for subspace tracking.About:
This article is published in Automatica.The article was published on 2000-11-01. It has received 207 citations till now. The article focuses on the topics: Linear subspace & Subspace topology.read more
Citations
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Subspace model identification-Part 1. The output-error state-space model identification class of algorithms
TL;DR: Two novel algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data are presented: an RQ factorization followed by a singular value decomposition and the solution of an overdetermined set of equations.
Journal ArticleDOI
Identification of piecewise affine systems via mixed-integer programming
TL;DR: This paper provides algorithms based on mixed-integer linear or quadratic programming which are guaranteed to converge to a global optimum of hybrid dynamical systems, and suggests a way of trading off between optimality and complexity by using a change detection approach.
Journal ArticleDOI
Hierarchical gradient-based identification of multivariable discrete-time systems
Feng Ding,Tongwen Chen +1 more
TL;DR: A hierarchical gradient iterative algorithm and a hierarchical stochastic gradient algorithm are proposed and it is proved that the parameter estimation errors given by the algorithms converge to zero for any initial values under persistent excitation.
Journal ArticleDOI
Hierarchical identification of lifted state-space models for general dual-rate systems
Feng Ding,Tongwen Chen +1 more
TL;DR: This paper derives the lifted state-space models, and presents combined parameter and state estimation algorithms for identifying the canonical lifted models based on the given dual-rate input-output data, taking into account the causality constraints of the lifted systems.
Book ChapterDOI
Subspace identification of Hammerstein systems using least squares support vector machines
TL;DR: This paper presents a method for the identification of multiple-input-multiple-output (MIMO) Hammerstein systems for the goal of prediction by rewriting the oblique projection in the N4SID algorithm as a set of componentwise least squares support vector machines (LS-SVMs) regression problems.
References
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Journal ArticleDOI
Projection approximation subspace tracking
TL;DR: A novel interpretation of the signal subspace as the solution of a projection like unconstrained minimization problem is presented, and it is shown that recursive least squares techniques can be applied to solve this problem by making an appropriate projection approximation.
Journal ArticleDOI
Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
TL;DR: Two algorithms to identify a linear, time-invariant, finite dimensional state space model from input-output data and a special case of the recently developed Multivariable Output-Error State Space (MOESP) class of algorithms based on instrumental variables are described.
Subspace model identification-Part 1. The output-error state-space model identification class of algorithms
TL;DR: Two novel algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data are presented: an RQ factorization followed by a singular value decomposition and the solution of an overdetermined set of equations.
Related Papers (5)
Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
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