Abstract
Adaptive infinite impulse response (IIR) systems are widely used in modeling real world problems. This ubiquitous class of models requires less parameters and evince better performance over finite impulse response (FIR) systems. In the present paper, the system identification problem of IIR models is translated into a nonlinear optimization problem and a recently introduced population based algorithm, harmony search (HS), is adopted to cope with the identification problem. Furthermore, the performance of the proposed methodology is compared with two well-known meta-heuristic algorithms, genetic algorithm (GA) and particle swarm optimization (PSO). The identification results pertaining to two benchmark IIR systems are included which demonstrate that the proposed method is superior to GA and PSO based algorithms in terms of convergence speed and estimation accuracy.
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