iir system identification using improved harmony search algorithm with chaos

due to the fact that the error surface of adaptive infinite impulse response (iir) systems is generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such systems. in this case, global optimization techniques are required in order to avoid the 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, the system identification problem of iir models is formulated as a nonlinear optimization problem and then an improved version of harmony search incorporating chaotic search (cihs), is introduced to solve the identification problem of four benchmark iir systems. furthermore, the performance of the proposed methodology is compared with hs and two well-known meta-heuristic algorithms, genetic algorithm (ga) and particle swarm optimization (pso) and a modified version of pso called psow. the results demonstrate that the proposed method has the superior performance over the other above mentioned algorithms in terms of convergence speed and accuracy.