RT - Journal Article
T1 - Nonlinear System Parameterization and Control using Reduced Adaptive Kernel Algorithm
JF - IUST
YR - 2022
JO - IUST
VO - 33
IS - 4
UR - http://ijiepr.iust.ac.ir/article-1-1572-en.html
SP - 1
EP - 16
K1 - Kernel adaptive filtering
K1 - Nonlinear system indentation
K1 - Least-mean square
K1 - Kernel least-mean square
K1 - Single input
K1 - Single-output (SISO) System
K1 - Gaussian kernel
AB - To develop a system for specific purpose, it needs to estimate its parameters (parameterization). It can be used in different fields like engineering, industry etc. In this work, authors used adaptive algorithm to model a system that is applicable in industry for control. This adaptive model is non-linear where its estimation is based on kernel based Least-mean square (LMS) algorithm. The kernel used as Polynomial and Gaussian. As the system is nonlinear polynomial kernel-based algorithm fails to prove its efficacy, though it is of low complexity approach. Gaussian kernel-based application for nonlinear system control performance better as compared to polynomial kernel. Further its complexity is reduced and used for faster performance. The result shows its performance in form of MSE, MAE, RMSE for identification and control that is very useful in industrial application.
LA eng
UL http://ijiepr.iust.ac.ir/article-1-1572-en.html
M3 10.22068/ijiepr.33.4.4
ER -