Robust estimation of IIR system's parameter using craziness based particle swarm optimization

Patnaik, Asutosh; Panda, Meenakshi; Dash, Meera; Panigrahi, Trilochan; Sharma, Renu

doi:10.1109/mami.2017.8307873

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Robust Fitness Function1

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2019

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“…The robust function h is designed in such a way that it gives less weight to data points which deviate greatly from the desired parameter (error is high when impulsive noise occur), w. The cost function (9) is modified for a robust estimation and given as (Dash et al, 2017b;Patnaik et al, 2017)…”

Section: Robust Fitness Functionmentioning

confidence: 99%

Robust estimation of IIR system's parameter using modified particle swarm optimisation algorithm

Dash¹,

Panigrahi²,

Sharma³

2019

IJCISTUDIES

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This paper introduces a novel method of robust parameter estimation of infinite impulse response (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 cost functions are used here. It is a fact that the IIR system falls in local minima. Thus the gradien-based algorithm which is good for finite impulse response (FIR) system, can not be used for IIR. Therefore the parameters of the IIR system is estimated using modified particle swarm optimisation algorithm. The most used and analysed robust cost functions such as Hubers and saturation nonlinearity function are used in the optimisation algorithm. The simulation results show that the proposed robust algorithms are providing better performance than the Wilcoxon norm-based robust algorithm and conventional error squared-based PSO algorithms.

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Section: Robust Fitness Functionmentioning

confidence: 99%