Step Size Optimization of LMS Algorithm Using Particle Swarm Optimization Algorithm in System Identification
System identification is the art and science of building mathematical models of dynamic systems from observed input-output 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.