IIR system identification using Particle Swarm Optimization with Improved Inertia Weight approach

In this paper a modified version of swarm intelligence technique called Particle Swarm Optimization with Improved Inertia Weight (PSOIIW) approach is applied to IIR adaptive system identification problem. The proposed technique PSOIIW performs a structured randomized search of an unknown parameter within a multidimensional search space by manipulating a swarm of particles to converge to an optimal solution. In this technique iteration based inertia weight is calculated individually for each particle that results in better search within the multidimensional search space. The exploration and exploitation of entire search space can be handled efficiently with the proposed PSOIIW along with the benefits of overcoming the premature convergence and stagnation problems. The simulation results justify the optimization efficacy of the proposed PSOIIW over RGA and PSO.