Identification of Minimal Order State Space Models from Stochastic Input-Output Data

1988 ◽  
Vol 26 (1) ◽  
pp. 56-65 ◽  
Author(s):  
Y. Baram ◽  
B. Porat
Keyword(s):  
State Space ◽  
State Space Models ◽  
Input Output ◽  
Minimal Order ◽  
Output Data ◽  
Stochastic Input ◽  
Order State

IDENTIFICATION OF MULTISCALE STATE–SPACE MODELS FROM INPUT–OUTPUT DATA

2015 ◽  
Vol 43 (1) ◽  
Author(s):  
Marcelo N. Martins ◽  
Ronaldo Waschburger ◽  
Roberto K.H. Galvão
Keyword(s):  
State Space ◽  
State Space Models ◽  
Input Output ◽  
Output Data

Heat transfer modeling and identification using fractional order state space models

2008 ◽  
Vol 42 (6-8) ◽  
pp. 939-951 ◽  
Author(s):  
Tounsia Jamah ◽  
Rachid Mansouri ◽  
Saïd Djennoune ◽  
Maâmar Bettayeb
Keyword(s):  
Heat Transfer ◽  
State Space ◽  
Fractional Order ◽  
State Space Models ◽  
Heat Transfer Modeling ◽  
Order State

scholarly journals Comparing global input-output behavior of frozen-equivalent LPV state-space models

2017 ◽  
Vol 50 (1) ◽  
pp. 9766-9771 ◽  
Author(s):  
Ziad Alkhoury ◽  
Mihály Petreczky ◽  
Guillaume Mercère
Keyword(s):  
State Space ◽  
State Space Models ◽  
Input Output ◽  
Output Behavior

Identification of highly accurate low order state space models in the frequency domain

1996 ◽  
Vol 52 (2) ◽  
pp. 195-207 ◽  
Author(s):  
Robert N. Jacques ◽  
Ketao Liu ◽  
David W. Miller
Keyword(s):  
State Space ◽  
Frequency Domain ◽  
State Space Models ◽  
Order State ◽  
Low Order

scholarly journals Parameter and State Estimator for State Space Models

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ruifeng Ding ◽  
Linfan Zhuang
Keyword(s):  
State Space ◽  
State Equation ◽  
The State ◽  
Parameter Estimates ◽  
Identification Algorithm ◽  
State Variables ◽  
Input Output ◽  
State Estimator ◽  
Output Data ◽  
Canonical State

This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.

Identification of Highly Accurate Low Order State Space Models in the Frequency Domain

1996 ◽  
Vol 29 (1) ◽  
pp. 4249-4254
Author(s):  
Robert N. Jacques ◽  
Ketao Liu ◽  
David W. Miller
Keyword(s):  
State Space ◽  
Frequency Domain ◽  
State Space Models ◽  
Order State ◽  
Low Order
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