In state-space system identification theory, the Hankel matrix often appears prior to model realization. Traditionally, one identifies from input-output data the Markov parameters from which the Hankel matrix is built. This paper examines the strategy where the Hankel matrix itself is identified from input-output data. Various options are examined along this direction where the identification of the Hankel matrix can be carried out directly or indirectly. Advantages and drawbacks associated with each option are examined and discussed. Extensive evaluation both with simulated and experimental data indicates that the new approach is effective in detecting the “true ” or effective order of the system, hence it is capable of producing relativel...
This paper presents theory and algorithms for system identification suitable for the framework of be...
The identification of affinely parameterized state–space system models is quite popular to model pra...
. Traditional prediction-error techniques for multivariable system identification require canonical ...
A geometrically inspired matrix algorithm is derived for the identification of statespace models for...
A geometrically inspired matrix algorithm is derived for the identification of state space models fo...
AbstractThis letter studies identification problems of model orders using the Hankel matrix of impul...
Subspace-based State Space System IDentification (4SID) methods have recently been proposed as an al...
In this paper, subspace identification methods are proposed to analyze the differences between On-An...
In this paper we consider identification of multivariable linear systems using state-space models. A...
We present the basic notions on subspace identification algorithms for linear systems. These methods...
: We give a general overview of the state-of-the-art in subspace system identification methods. We h...
A new subspace algorithm consistently identifies stochastic state space models directly from given o...
In this work a dynamic state-space model was constructed using a Hankel matrix formulation. A novel ...
This article and its companion study constitute a two part attempt at providing a common framework f...
This paper and its companion study constitute a two-part attempt at providing a common framework for...
This paper presents theory and algorithms for system identification suitable for the framework of be...
The identification of affinely parameterized state–space system models is quite popular to model pra...
. Traditional prediction-error techniques for multivariable system identification require canonical ...
A geometrically inspired matrix algorithm is derived for the identification of statespace models for...
A geometrically inspired matrix algorithm is derived for the identification of state space models fo...
AbstractThis letter studies identification problems of model orders using the Hankel matrix of impul...
Subspace-based State Space System IDentification (4SID) methods have recently been proposed as an al...
In this paper, subspace identification methods are proposed to analyze the differences between On-An...
In this paper we consider identification of multivariable linear systems using state-space models. A...
We present the basic notions on subspace identification algorithms for linear systems. These methods...
: We give a general overview of the state-of-the-art in subspace system identification methods. We h...
A new subspace algorithm consistently identifies stochastic state space models directly from given o...
In this work a dynamic state-space model was constructed using a Hankel matrix formulation. A novel ...
This article and its companion study constitute a two part attempt at providing a common framework f...
This paper and its companion study constitute a two-part attempt at providing a common framework for...
This paper presents theory and algorithms for system identification suitable for the framework of be...
The identification of affinely parameterized state–space system models is quite popular to model pra...
. Traditional prediction-error techniques for multivariable system identification require canonical ...