Step Size Optimization of LMS Algorithm Using Particle Swarm Optimization Algorithm in System Identification

Summary System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data .This paper combines Particle Swarm Optimization Algorithm and LMS algorithm to describe the application of a Particle swarm Optimization (PSO) to the problem of parameter optimization for an adaptive Finite Impulse Response (FIR) filter. LMS algorithm computes the filter coefficients and PSO search the optimal step-size adaptively. Because step-size influences on the stability and performance, so it is necessary to apply method that can control it.. However, the statistical Least Mean Squares method is faster than the genetic algorithm. For this reason we suggest using the genetic algorithm for off-line applications, and the statistical method for on-line adaptation. A hybrid method combining the advantages of both methods is proposed for real world applications.