scholarly journals Auxiliary Model Based Multi-Innovation Stochastic Gradient Identification Algorithm for Periodically Non-Uniformly Sampled-Data Hammerstein Systems

Algorithms ◽  
2017 ◽  
Vol 10 (3) ◽  
pp. 84
Author(s):  
Li Xie ◽  
Huizhong Yang
Keyword(s):  
Stochastic Gradient ◽  
Identification Algorithm ◽  
Sampled Data ◽  
Auxiliary Model ◽  
Hammerstein Systems ◽  
Model Based

scholarly journals Two Identification Methods for Dual-Rate Sampled-Data Nonlinear Output-Error Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jing Chen ◽  
Ruifeng Ding
Keyword(s):  
Stochastic Gradient ◽  
The Other ◽  
Identification Algorithm ◽  
Sampled Data ◽  
Unknown Parameters ◽  
Auxiliary Model ◽  
Identification Methods ◽  
Numerical Example ◽  
Output Error ◽  
Polynomial Transformation

This paper presents two methods for dual-rate sampled-data nonlinear output-error systems. One method is the missing output estimation based stochastic gradient identification algorithm and the other method is the auxiliary model based stochastic gradient identification algorithm. Different from the polynomial transformation based identification methods, the two methods in this paper can estimate the unknown parameters directly. A numerical example is provided to confirm the effectiveness of the proposed methods.

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.

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