Convergence Analysis of Forgetting Factor Least Squares Algorithm for ARMAX Time-Delay Models

被引：0
作者：
Saïda Bedoui
Kamel Abderrahim
机构：
[1] University of Gabes,Research Laboratory of Numerical Control of Industrial Processes, National Engineering School of Gabes
来源：
Circuits, Systems, and Signal Processing | 2023年 / 42卷
关键词：
ARMAX model; Sample delay; Identification; Convergence analysis; Least squares;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, the estimation problem is considered for both sample delay and coefficients of ARMAX model. An extended recursive least squares algorithm is derived by minimizing a quadratic cost function. However, the solution of the optimization problem returns a real value for the sample delay. To overcome this difficulty, the rounding properties are used to transform the integer nonlinear problem into a real optimization problem. In addition, consistency of the estimates with their convergence rates are established under the persistent excitation condition. Finally, experimental results on semi-batch reactor are presented to illustrate the performance of the proposed method.
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页码：405 / 430
页数：25
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