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.

scholarly journals Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Huiyi Hu ◽  
Xiao Yongsong ◽  
Rui Ding
Keyword(s):  
Nonlinear System ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
System Model ◽  
Noise Model ◽  
Identification Algorithm ◽  
Separation Principle ◽  
Model Decomposition ◽  
Stochastic Gradient Algorithm ◽  
Input Nonlinear System

An input nonlinear system is decomposed into two subsystems, one including the parameters of the system model and the other including the parameters of the noise model, and a multi-innovation stochastic gradient algorithm is presented for Hammerstein controlled autoregressive autoregressive (H-CARAR) systems based on the key term separation principle and on the model decomposition, in order to improve the convergence speed of the stochastic gradient algorithm. The key term separation principle can simplify the identification model of the input nonlinear system, and the decomposition technique can enhance computational efficiencies of identification algorithms. The simulation results show that the proposed algorithm is effective for estimating the parameters of IN-CARAR systems.

scholarly journals DATA FILTERING BASED STOCHASTIC GRADIENT ALGORITHMS FOR MULTIVARIABLE CARAR-LIKE SYSTEMS

2013 ◽  
Vol 18 (3) ◽  
pp. 374-385 ◽  
Author(s):  
Dongqing Wang ◽  
Tong Shan ◽  
Rui Ding
Keyword(s):  
Colored Noise ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
Data Filtering ◽  
Gradient Estimation ◽  
Identification Problems ◽  
Hierarchical Identification ◽  
Estimation Algorithms ◽  
Stochastic Gradient Algorithm ◽  
Gradient Algorithms

This paper considers identification problems for a multivariable controlled autoregressive system with autoregressive noises. A hierarchical generalized stochastic gradient algorithm and a filtering based hierarchical stochastic gradient algorithm are presented to estimate the parameter vectors and parameter matrix of such multivariable colored noise systems, by using the hierarchical identification principle. The simulation results show that the proposed hierarchical gradient estimation algorithms are effective.

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