Partially coupled extended stochastic gradient algorithm for nonlinear multivariable output error moving average systems

2017 ◽  
Vol 34 (2) ◽  
pp. 629-647 ◽  
Author(s):  
Xuehai Wang ◽  
Feng Ding
Keyword(s):  
Parameter Estimation ◽  
Moving Average ◽  
Estimation Algorithm ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
Parameter Estimates ◽  
Multivariable Systems ◽  
Content Type ◽  
Stochastic Gradient Algorithm ◽  
Output Error

Purpose The purpose of this paper is to study the parameter estimation problem of nonlinear multivariable output error moving average systems. Design/methodology/approach A partially coupled extended stochastic gradient algorithm is presented for nonlinear multivariable systems by using the decomposition technique. Findings The proposed algorithm can realize the coupled computation of the parameter estimates between subsystems. Originality/value This paper develops a coupled parameter estimation algorithm for nonlinear multivariable systems and directly estimates the system parameters without over-parameterization.

scholarly journals Least-Squares Parameter Estimation Algorithm for a Class of Input Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Weili Xiong ◽  
Wei Fan ◽  
Rui Ding
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Least Squares ◽  
Moving Average ◽  
Estimation Algorithm ◽  
Autoregressive Moving Average ◽  
Parameter Estimates ◽  
Persistent Excitation ◽  
Estimation Algorithms ◽  
Parameter Estimation Algorithm

This paper studies least-squares parameter estimation algorithms for input nonlinear systems, including the input nonlinear controlled autoregressive (IN-CAR) model and the input nonlinear controlled autoregressive autoregressive moving average (IN-CARARMA) model. The basic idea is to obtain linear-in-parameters models by overparameterizing such nonlinear systems and to use the least-squares algorithm to estimate the unknown parameter vectors. It is proved that the parameter estimates consistently converge to their true values under the persistent excitation condition. A simulation example is provided.

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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