Recursive Nonlinear System Identification by a Stochastic Gradient Algorithm: Stability, Performance, and Model Nonlinearity Considerations

2004 ◽  
Vol 52 (9) ◽  
pp. 2540-2550 ◽  
Author(s):  
D. Levanony ◽  
N. Berman
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
Stochastic Gradient Algorithm ◽  
Stability Performance ◽  
Algorithm Stability

scholarly journals Auxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm for Hammerstein Output-Error Systems

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 247
Author(s):  
Chen Xu ◽  
Yawen Mao
Keyword(s):  
Nonlinear System Identification ◽  
Identification Problem ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
Estimation Accuracy ◽  
Basic Premise ◽  
Auxiliary Model ◽  
Stochastic Gradient Algorithm ◽  
Output Error ◽  
Model Based

This paper focuses on the nonlinear system identification problem, which is a basic premise of control and fault diagnosis. For Hammerstein output-error nonlinear systems, we propose an auxiliary model-based multi-innovation fractional stochastic gradient method. The scalar innovation is extended to the innovation vector for increasing the data use based on the multi-innovation identification theory. By establishing appropriate auxiliary models, the unknown variables are estimated and the improvement in the performance of parameter estimation is achieved owing to the fractional-order calculus theory. Compared with the conventional multi-innovation stochastic gradient algorithm, the proposed method is validated to obtain better estimation accuracy by the simulation results.

scholarly journals Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Huiyi Hu ◽  
Xiao Yongsong ◽  
Rui Ding
Keyword(s):  
Nonlinear System ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
System Model ◽  
Noise Model ◽  
Identification Algorithm ◽  
Separation Principle ◽  
Model Decomposition ◽  
Stochastic Gradient Algorithm ◽  
Input Nonlinear System

An input nonlinear system is decomposed into two subsystems, one including the parameters of the system model and the other including the parameters of the noise model, and a multi-innovation stochastic gradient algorithm is presented for Hammerstein controlled autoregressive autoregressive (H-CARAR) systems based on the key term separation principle and on the model decomposition, in order to improve the convergence speed of the stochastic gradient algorithm. The key term separation principle can simplify the identification model of the input nonlinear system, and the decomposition technique can enhance computational efficiencies of identification algorithms. The simulation results show that the proposed algorithm is effective for estimating the parameters of IN-CARAR systems.

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