Hierarchical Maximum Likelihood Stochastic Gradient Identification Algorithm for Feedback Nonlinear Systems Using the Data Filtering Technique

2018 ◽  
Author(s):  
Junhong Li ◽  
Xiao Li ◽  
Juping Gu ◽  
Yi Yang
Keyword(s):  
Maximum Likelihood ◽  
Nonlinear Systems ◽  
Stochastic Gradient ◽  
Identification Algorithm ◽  
Data Filtering ◽  
Filtering Technique

scholarly journals Filtering-Based Parameter Identification Methods for Multivariable Stochastic Systems

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2254
Author(s):  
Huafeng Xia ◽  
Feiyan Chen
Keyword(s):  
Maximum Likelihood ◽  
Stochastic Systems ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
Parameter Estimates ◽  
Data Filtering ◽  
Model Decomposition ◽  
Stochastic Gradient Algorithm ◽  
Equation Error ◽  
Identification Theory

This paper presents an adaptive filtering-based maximum likelihood multi-innovation extended stochastic gradient algorithm to identify multivariable equation-error systems with colored noises. The data filtering and model decomposition techniques are used to simplify the structure of the considered system, in which a predefined filter is utilized to filter the observed data, and the multivariable system is turned into several subsystems whose parameters appear in the vectors. By introducing the multi-innovation identification theory to the stochastic gradient method, this study produces improved performances. The simulation numerical results indicate that the proposed algorithm can generate more accurate parameter estimates than the filtering-based maximum likelihood recursive extended stochastic gradient algorithm.

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