A particle swarm optimization approach to nonlinear rational filter modeling

This paper presents a particle swarm optimization (PSO) algorithm to solve the parameter estimation problem for nonlinear dynamic rational filters. For the modeling of the nonlinear rational filter, the unknown filter parameters are arranged in the form of a parameter vector which is called a particle in the terminology of PSO. The proposed PSO algorithm applies the velocity updating and position updating formulas to the population composed of many particles such that better particles are generated. Because the PSO algorithm manipulates the parameter vectors directly as real numbers rather than binary strings, implementing the PSO algorithm into the computer programs becomes fairly easy and straightforward. Finally, an illustrative example for the modeling of the nonlinear rational filter is provided to show the validity, as compared with the traditional genetic algorithm, of the proposed method.