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

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.