Particle swarm optimization with quantum infusion for system identification

System identification is a challenging and complex optimization problem due to nonlinearity of the systems and even more in a dynamic environment. Adaptive infinite impulse response (IIR) systems are preferably used in modeling real world systems because of their reduced number of coefficients and better performance over the finite impulse response filters. Particle swarm optimization (PSO) and its other variants has been a subject of research for the past few decades for solving complex optimization problems. In this paper, PSO with quantum infusion (PSO-QI) is used in identification of benchmark IIR systems and a real world problem in power systems. PSO-QI's performance is compared with PSO and differential evolution PSO (DEPSO) algorithms. The results show that PSO-QI has better performance over these algorithms in identifying dynamical systems.

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