The hierarchical gradient based iterative identification algorithm for multi-input output-error autoregressive systems

2017 ◽  
Author(s):  
Jiling Ding
Keyword(s):  
Identification Algorithm ◽  
Input Output ◽  
Iterative Identification ◽  
Output Error ◽  
Gradient Based

scholarly journals The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jiling Ding
Keyword(s):  
Least Squares ◽  
Identification Problem ◽  
Parameter Estimates ◽  
Identification Algorithm ◽  
Input Output ◽  
Iterative Identification ◽  
Hierarchical Identification ◽  
Output Error ◽  
Gradient Based ◽  
Simulation Results

This paper considers the identification problem of multi-input-output-error autoregressive systems. A hierarchical gradient based iterative (H-GI) algorithm and a hierarchical least squares based iterative (H-LSI) algorithm are presented by using the hierarchical identification principle. A gradient based iterative (GI) algorithm and a least squares based iterative (LSI) algorithm are presented for comparison. The simulation results indicate that the H-LSI algorithm can obtain more accurate parameter estimates than the LSI algorithm, and the H-GI algorithm converges faster than the GI algorithm.

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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