Adaptive filtering via particle swarm optimization

This paper introduces the application of particle swarm optimization techniques to generalized adaptive nonlinear and recursive filter structures. Particle swarm optimization (PSO) is a population based optimization algorithm, similar to the genetic algorithm (GA), that performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge on a suitable solution. These types of structured stochastic search techniques are independent of the adaptive filter structure and are capable of converging on the global solution for multimodal optimization problems, which makes them especially useful for optimizing nonlinear and infinite impulse response (IIR) adaptive filters. This paper outlines PSO for adaptive filtering and provides a comparison to the GA for various IIR and nonlinear filter structures.