The parameter estimation problem of the ARX model is studied in this paper. First, some traditional identification algorithms are briefly introduced, and then a new parameter estimation algorithm—the modified momentum gradient descent algorithm—is developed. Two gradient directions with their corresponding step sizes are derived in each iteration. Compared with the traditional parameter identification algorithms, the modified momentum gradient descent algorithm has a faster convergence rate. A simulation example shows that the proposed algorithm is effective
System identification with binary or quantized measurements is a problem relevant to a number of app...
Abstract—A linear and nonlinear autoregressive (AR) moving average (MA) (ARMA) identification algori...
System identification with binary or quantized measurements is a problem relevant to a number of app...
A robust standard gradient descent (SGD) algorithm for ARX models using the Aitken acceleration meth...
AbstractAn extended stochastic gradient algorithm is developed to estimate the parameters of Hammers...
A robust standard gradient descent (SGD) algorithm for ARX models using the Aitken acceleration meth...
A robust standard gradient descent (SGD) algorithm for ARX models using the Aitken acceleration meth...
AbstractThis paper studies the convergence of the stochastic gradient identification algorithm of mu...
This paper describes a very simple and intuitive algorithm to estimate parameters of ARX models from...
This paper describes a very simple and intuitive algorithm to estimate parameters of ARX models from...
It is well-known that mathematical models are the basis for system analysis and controller design. T...
Abstract – Systems that exist in nature are a combination of linear and nonlinear elements, and ther...
AbstractAn extended stochastic gradient algorithm is developed to estimate the parameters of Hammers...
– Systems that exist in nature are a combination of linear and nonlinear elements, and there are sti...
System identification with binary or quantized measurements is a problem relevant to a number of app...
System identification with binary or quantized measurements is a problem relevant to a number of app...
Abstract—A linear and nonlinear autoregressive (AR) moving average (MA) (ARMA) identification algori...
System identification with binary or quantized measurements is a problem relevant to a number of app...
A robust standard gradient descent (SGD) algorithm for ARX models using the Aitken acceleration meth...
AbstractAn extended stochastic gradient algorithm is developed to estimate the parameters of Hammers...
A robust standard gradient descent (SGD) algorithm for ARX models using the Aitken acceleration meth...
A robust standard gradient descent (SGD) algorithm for ARX models using the Aitken acceleration meth...
AbstractThis paper studies the convergence of the stochastic gradient identification algorithm of mu...
This paper describes a very simple and intuitive algorithm to estimate parameters of ARX models from...
This paper describes a very simple and intuitive algorithm to estimate parameters of ARX models from...
It is well-known that mathematical models are the basis for system analysis and controller design. T...
Abstract – Systems that exist in nature are a combination of linear and nonlinear elements, and ther...
AbstractAn extended stochastic gradient algorithm is developed to estimate the parameters of Hammers...
– Systems that exist in nature are a combination of linear and nonlinear elements, and there are sti...
System identification with binary or quantized measurements is a problem relevant to a number of app...
System identification with binary or quantized measurements is a problem relevant to a number of app...
Abstract—A linear and nonlinear autoregressive (AR) moving average (MA) (ARMA) identification algori...
System identification with binary or quantized measurements is a problem relevant to a number of app...