Applying Modified Discrete Particle Swarm Optimization Algorithm and Genetic Algorithm for system identification

A system identification problem can be formulated as an optimization task where the objectives are to find a model and a set of parameters that minimize the prediction error between the plant output and the model output. This paper presents a technique for identifying the parameters of system using Genetic Algorithms and the Modified Discrete Particle Swarm Optimization Algorithm. Derived from a step test a robust identification method for process is proposed. The simulation results show suggested methods are robust in the presence of large amounts of measurement noise, and discrete particle swarm optimization algorithm has a lower cost value than Genetic Algorithm.