Fractional order FIR differentiator design using particle swarm optimization algorithm

Abstract

In this paper, a population?based evolutionary optimization technique called particle swarm optimization (PSO) is applied for the optimization of system coefficients of the finite impulse response?fractional order differentiator (FIR?FOD) design problem. The conventional FIR?FOD design methods are not efficient for nonlinear, nonuniform, and multimodal design problem due to getting trapped in local optimal solution. To overcome this problem, global optimization techniques are required. The superior FIR?FOD design capability of the proposed method is evident from the results obtained through an exhaustive simulation study. Simulation results demonstrate that the proposed FOD design technique using PSO outperforms the genetic algorithm in terms of design accuracy (magnitude error and phase error), speed of convergence, and optimal solution. The simulation results have also been compared with those obtained by the conventional FOD design methods such as DFT interpolation, radial basis function (RBF) interpolation, DCT interpolation, and DST interpolation methods.