A study on finite-time particle swarm optimization as a system identification method

The performance as a system identification technique of a variant of the particle swarm optimization (PSO) algorithm named finite-time particle swarm optimization (FPSO) was studied. First, this method was compared to several system identification algorithms by using data from a simulated linear system model. Special attention was given to their performance when the data from which they estimate the parameters of the system contain measurement noise. Afterwards, the effectiveness of FPSO in estimating the parameters of nonlinear systems was evaluated, using both data from simulations and data obtained from a real system with nonlinear behavior. The FPSO algorithm showed excellent performance when estimating the parameters of the simulated linear and nonlinear systems, both with noisy and noiseless data. Results from the parameter estimation of the real system showed more variation in the results of the algorithm; however, simulations using the estimated parameters were still able to closely emulate the real system.