Auxiliary Model-Based Forgetting Factor Stochastic Gradient Algorithm for Dual-Rate Nonlinear Systems and its Application to a Nonlinear Analog Circuit

2014 ◽  
Vol 33 (6) ◽  
pp. 1957-1969 ◽  
Author(s):  
Xiangli Li ◽  
Lincheng Zhou ◽  
Ruifeng Ding
Keyword(s):  
Nonlinear Systems ◽  
Analog Circuit ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
Forgetting Factor ◽  
Auxiliary Model ◽  
Stochastic Gradient Algorithm ◽  
Model Based ◽  
Nonlinear Analog

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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