Basis of invariants and canonical forms for linear dynamic systems
Automatica (Journal of IFAC)
The determination of state-space representations for linear multivariable systems
Automatica (Journal of IFAC)
Convergence of stochastic gradient estimation algorithm for multivariable ARX-like systems
Computers & Mathematics with Applications
Invariants and canonical forms for systems structural and parametric identification
Automatica (Journal of IFAC)
Brief paper: A general algorithm for determining state-space representations
Automatica (Journal of IFAC)
Paper: A study of MBH-type realization algorithms
Automatica (Journal of IFAC)
Brief paper: An approach to multivariable system identification
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Paper: An instrumental variable method for model order identification
Automatica (Journal of IFAC)
Brief paper: On the recursive identification of multi-input, multi-output systems
Automatica (Journal of IFAC)
Brief paper: On the synthesis of linear control systems with specified characteristics
Automatica (Journal of IFAC)
Brief paper: Recursive estimation of the parameters of linear multivariable systems
Automatica (Journal of IFAC)
Brief paper: Suboptimal control of linear stochastic multivariable systems with unknown parameters
Automatica (Journal of IFAC)
On-line structure selection for multivariable state-space models
Automatica (Journal of IFAC)
Papers: Identification of stochastic linear systems in presence of input noise
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
On the identification of continuous-time multivariable systems from samples of input-output data
Mathematical and Computer Modelling: An International Journal
Hi-index | 22.18 |
In this paper a unitary identification procedure for linear multivariable systems is described. The approach considered is based on a preliminary canonical structure identification, i.e. on the direct determination, from input-output sequences, of a set of invariant indexes completely describing the input-output structure of the system. Reduced computation time and storage, canonical representation of the identified model and easy estimation of the initial state of the system are in this way achieved. The results obtained from the application of this procedure to a simulated process and to a chemical reactor are also reported.