Multierror stochastic gradient algorithm for identification of a Hammerstein system with random noise and its application in the modeling of a continuous stirring tank reactor

2021 ◽  
Author(s):  
Shaoxue Jing
Keyword(s):  
Random Noise ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
Hammerstein System ◽  
Tank Reactor ◽  
Stochastic Gradient Algorithm

Identification of Hammerstein Systems with Two-Segment Preload Nonlinearity Based on the Gradient Search

2014 ◽  
Vol 651-653 ◽  
pp. 2314-2317 ◽  
Author(s):  
Hong Fen Zou
Keyword(s):  
Convergence Rate ◽  
Special Form ◽  
Approximation Theorem ◽  
Stochastic Gradient ◽  
Gradient Algorithm ◽  
Gradient Search ◽  
Unknown Parameters ◽  
Hammerstein System ◽  
Hammerstein Systems ◽  
Stochastic Gradient Algorithm

This paper deals with the parameter identication problem for Hammerstein systems with two-segment preload nonlinearity. Taking into account the complexity of Hammerstein systems, we use theWeierstrass approximation theorem to convert a Hammerstein system into a special form that has linear-in-parameters, and propose a stochastic gradient algorithm to estimate all unknown parameters of Hammerstein systems. Furthermore, a modified stochastic gradient algorithm is given to improve the convergence rate. The applicability of the approach is illustrated by a simulation example.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

Sign in / Sign up

Export Citation Format

Share Document