Abstract
In this paper, a new evolutionary optimization technique called flower pollination algorithm is employed to compute the optimal parameters of an unknown infinite impulse response (IIR) system. FPA is inspired by the pollination process of flowers. To achieve a balance between intensification and diversification phases, proper tuning of control parameter has been performed. The proposed FPA based method for system identification is free from the problems of premature convergence and suboptimal solutions encountered in conventional methods. To validate the performance of FPA based system identification, simulations has been carried out for three benchmarked IIR systems using same order system. Mean square error (MSE) and convergence profile are the performance measures used to assess the performance of the proposed method. Simulation results demonstrate that the FPA based system identification gives superior performance as compared to system identification achieved with genetic algorithm (GA) and particle swarm optimization (PSO).
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