Auxiliary model-based least-squares identification methods for Hammerstein output-error systems

2007 ◽  
Vol 56 (5) ◽  
pp. 373-380 ◽  
Author(s):  
Feng Ding ◽  
Yang Shi ◽  
Tongwen Chen
Keyword(s):  
Least Squares ◽  
Auxiliary Model ◽  
Identification Methods ◽  
Output Error ◽  
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.

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