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Robust Estimation of IIR System’s Parameter Using Adaptive Particle Swarm Optimization Algorithm

  • Meera DashEmail author
  • Trilochan Panigrahi
  • Renu Sharma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)

Abstract

This paper introduces a novel method of robust parameter estimation of IIR system. When training signal contains strong outliers, the conventional squared error-based cost function fails to provide desired performance. Thus, a computationally efficient robust Hubers cost function is used here. As we know that the IIR system falls in local minima, gradient-based algorithm cannot be used. Therefore, the parameters of the IIR system are estimated using adaptive particle swarm optimization algorithm with Hubers cost function. The simulation results show that the proposed algorithm provides better performance than Wilcoxon norm-based robust algorithm and conventional error squared based PSO algorithm.

Keywords

IIR system Impulsive noise Robust estimation Wilcoxon norm Hubers cost function Adaptive particle swarm optimization 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of ECEITER Siksha ‘O’ Anusandhan UniversityBhubaneswarIndia
  2. 2.Department of ECENational Institute of Technology GoaPondaIndia
  3. 3.Department of EEITER Siksha ‘O’ Anusandhan UniversityBhubaneswarIndia

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