Due to the fact that the error surface of adaptive infinite impulse response (IIR) systems is generally nonlinear and multimodal, conventional derivative-based techniques fail when used in adaptive Filter design. In this sense, global optimization techniques are required in order to avoid local minima. Harmony search (HS), a musical inspired metaheuristic, is a recently introduced population-based algorithm that has been successfully applied to global optimization problems. In the present paper, adaptive IIR filtering is formulated as a nonlinear optimization problem and then an improved version of HS incorporating chaotic search (CIHS) is introduced to solve the identification problem of three benchmark IIR systems. Furthermore, the performance of the proposed methodology is compared with HS and two well-known metaheuristic algorithms, genetic algorithm (GA) and particle swarm optimization (PSO) and a modified version of PSO called PSOW (Particle Swarm Optimization with weight Factor). The results demonstrate that the proposed method has superior performance over the other above-mentioned algorithms in terms of convergence speed and accuracy.

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adaptive infinite impulse response iir systems musical inspired metaheuristic method methodology hs and two wellknown metaheuristic algorithms genetic algorithm ga local minima harmony search error surface modified version of pso global optimization techniques improved version of hs incorporating chaotic search cihs nonlinear optimization problem accuracy convergence speed psow particle swarm optimization with weight populationbased algorithm abovementioned algorithms

Mehrnoosh Shafaati,Hamed Mojallali,.IIR filter optimization using improved chaotic harmony search algorithm. 59 (3-4),.

Mehrnoosh Shafaati,Hamed Mojallali,"IIR filter optimization using improved chaotic harmony search algorithm" 59

Mehrnoosh Shafaati,Hamed Mojallali,"IIR filter optimization using improved chaotic harmony search algorithm"