An identification algorithm for Hammerstein systems using subspace method

2011 ◽  
Author(s):  
Kian Jalaleddini ◽  
Robert E. Kearney
Keyword(s):  
Identification Algorithm ◽  
Subspace Method ◽  
Hammerstein Systems

scholarly journals Instrumental Variable-Based OMP Identification Algorithm for Hammerstein Systems

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Shuo Zhang ◽  
Dongqing Wang ◽  
Yaru Yan
Keyword(s):  
Instrumental Variable ◽  
Matching Pursuit ◽  
Linear Part ◽  
Orthogonal Matching Pursuit ◽  
Variable Method ◽  
Identification Algorithm ◽  
Hammerstein System ◽  
Hammerstein Systems ◽  
Nonlinear Part ◽  
Instrumental Variable Method

Hammerstein systems are formed by a static nonlinear block followed by a dynamic linear block. To solve the parameterizing difficulty caused by parameter coupling between the nonlinear part and the linear part in a Hammerstein system, an instrumental variable method is studied to parameterize the Hammerstein system. To achieve in simultaneously identifying parameters and orders of the Hammerstein system and to promote the computational efficiency of the identification algorithm, a sparsity-seeking orthogonal matching pursuit (OMP) optimization method of compressive sensing is extended to identify parameters and orders of the Hammerstein system. The idea is, by the filtering technique and the instrumental variable method, to transform the Hammerstein system into a simple form with a separated nonlinear expression and to parameterize the system into an autoregressive model, then to perform an instrumental variable-based orthogonal matching pursuit (IV-OMP) identification method for the Hammerstein system. Simulation results illustrate that the investigated method is effective and has advantages of simplicity and efficiency.

A Frequency-Domain Identification Algorithm for MIMO Fractional Order Systems with Time-Delay in State

2011 ◽  
Vol 383-390 ◽  
pp. 4397-4404
Author(s):  
Zeng Liao ◽  
Cheng Peng ◽  
Yong Wang
Keyword(s):  
Genetic Algorithm ◽  
Time Delay ◽  
Frequency Domain ◽  
Fractional Order ◽  
Identification Algorithm ◽  
Fractional Order Systems ◽  
Subspace Method ◽  
Domain Identification ◽  
Frequency Domain Identification ◽  
Fractional Differential

The system identification problem of Multi-Input Multi-Output fractional order systems with Time-Delay is studied. A Frequency-Domain identification algorithm is presented, which combines genetic algorithm and subspace method for fractional order systems with time-delay in state. The genetic algorithm is used to identify fractional differential order and Time-Delay parameter. And the state space model is obtained by using frequency-domain subspace method when fractional differential order and time-delay parameter are fixed. Numerical simulation results validate the proposed algorithm.

scholarly journals Generalized Kernel Regression Estimate for the Identification of Hammerstein Systems

2007 ◽  
Vol 17 (2) ◽  
pp. 189-197 ◽  
Author(s):  
Grzegorz Mzyk
Keyword(s):  
Kernel Method ◽  
Random Noise ◽  
Kernel Regression ◽  
Convergence In Probability ◽  
Identification Algorithm ◽  
Nonparametric Estimate ◽  
Hammerstein System ◽  
Regression Estimate ◽  
Hammerstein Systems ◽  
Generalized Kernel

Generalized Kernel Regression Estimate for the Identification of Hammerstein SystemsA modified version of the classical kernel nonparametric identification algorithm for nonlinearity recovering in a Hammerstein system under the existence of random noise is proposed. The assumptions imposed on the unknown characteristic are weak. The generalized kernel method proposed in the paper provides more accurate results in comparison with the classical kernel nonparametric estimate, regardless of the number of measurements. The convergence in probability of the proposed estimate to the unknown characteristic is proved and the question of the convergence rate is discussed. Illustrative simulation examples are included.

Modified Stochastic Gradient Algorithm for Hammerstein Systems

2013 ◽  
Vol 336-338 ◽  
pp. 2320-2323
Author(s):  
Li Xing Lv ◽  
Jing Chen
Keyword(s):  
Approximation Theorem ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
Identification Algorithm ◽  
Unknown Parameters ◽  
Hammerstein Systems ◽  
Stochastic Gradient Algorithm ◽  
Weierstrass Approximation Theorem ◽  
Identification Model

This paper proposes a modified stochastic gradient algorithm for Hammerstein systems. By the Weierstrass approximation theorem, the model of the nonlinear Hammerstein systems be changed to an identification model, then based on the derived model, a modified stochastic gradient identification algorithm is used to estimate all the unknown parameters of the systems. An example is provided to show the effectiveness of the proposed algorithm.

Error vector choice in direct inversion in the iterative subspace method

10.1002/jcc.4 ◽  
1996 ◽  
Vol 17 (16) ◽  
pp. 1836-1847 ◽  
Author(s):  
Irina V. Ionova ◽  
Emily A. Carter
Keyword(s):  
Subspace Method ◽  
Error Vector ◽  
Direct Inversion
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