Performance Analysis of Orthogonal Gradient Sign Algorithm Using Spline-based Hammerstein Model for Smart Application

Authors

  • Suchada Sitjongsataporn Department of Electronic Engineering, Mahanakorn Institute of Innovation (MII), Faculty of Engineering and Technology, Mahanakorn University of Technology, 140 Cheumsamphan Rd., Nongchok, Bangkok, Thailand https://orcid.org/0000-0002-2357-2365
  • Sethakarn Prongnuch Department of Robotics Engineering, Faculty of Industrial Technology, Suan Sunandha Rajabhat University, 1 U-Thong Nok Rd., Dusit, Bangkok, Thailand https://orcid.org/0000-0002-4950-7078

DOI:

https://doi.org/10.13052/jmm1550-4646.18412

Keywords:

Hammerstein model, Spline adaptive filtering, Sign algorithm, Orthogonal gradient adaptive algorithm, nonlinear systems

Abstract

This paper presents a spline-based Hammerstein model for adaptive filtering based on a sign algorithm with the normalised orthogonal gradient algorithm. Spline-based Hammerstein architecture consists of an interpolation spline-based adaptive lookup table in the part of nonlinear filter and an adaptive finite impulse response filter used in the part of linear filter. Hammerstein spline adaptive filter (HSAF) is a nonlinear filter for the nonlinear systems among the advantages in the low computational cost and high performance. An adaptive lookup table and spline control points are determined and derived with the orthogonal gradient-based mechanism. Performance analysis in terms of convergence properties and mean square analysis based on the mean square error (MSE) constraint are proven by using the Taylor series expansion of the estimation error in the form of the excess MSE. Experimental results indicate the robust performance of the proposed algorithm can provide the better performance than the other models based on the conventional least mean square Hammerstein spline adaptive filtering algorithm.

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Author Biographies

Suchada Sitjongsataporn, Department of Electronic Engineering, Mahanakorn Institute of Innovation (MII), Faculty of Engineering and Technology, Mahanakorn University of Technology, 140 Cheumsamphan Rd., Nongchok, Bangkok, Thailand

Suchada Sitjongsataporn received the B.Eng. (First-class honours) and D.Eng. degrees in Electronic Engineering from Mahanakorn University of Technology, Bangkok, Thailand in 2002 and 2009. She has worked as lecturer at department of Electronic Engineering, Mahanakorn University of Technology since 2002. Currently, she is an Associate Professor and the Associate Dean for Research at Faculty of Engineering and Technology in Mahanakorn University of Technology. Her research interests are mathematical and statistical models in the area of adaptive signal processing for communications, networking, embedded system, image and video processing.

Sethakarn Prongnuch, Department of Robotics Engineering, Faculty of Industrial Technology, Suan Sunandha Rajabhat University, 1 U-Thong Nok Rd., Dusit, Bangkok, Thailand

Sethakarn Prongnuch received his B.Eng. degree in computer engineering from the Rajamangala University of Technology Phra Nakhon in Bangkok, Thailand in 2011, and the M.Eng. and D.Eng. degrees in Computer Engineering from the Mahanakorn University of Technology in Bangkok, Thailand in 2013 and 2019, respectively. He has worked as a lecturer at the department of Robotics Engineering at Faculty of Industrial Technology in Suan Sunandha Rajabhat University in Bangkok, Thailand since 2013. His research interests include computer architectures and systems, embedded system, and heterogeneous system architecture.

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Published

2022-03-21

Issue

Section

ICEAST 2020