Estimation of System Models by Swarm Intelligent Method

System identification is the theory and methods of establishing mathematical models of the systems. As one of the key issues of system and control science, system identification has been widely applied to the design and analysis of the control system. Accordingly, system identification becomes one of the current active subjects. Currently, parameter estimation of the nonlinear system models is a very important problem in the area of system identification. In this paper, the parameter estimation problems of the nonlinear system models are converted into a nonlinear function optimization problem over parameter space initially. Then, the estimates of the nonlinear system model parameters are gotten by using a modified fish swarm algorithm. Finally, in simulation, the presented identification method is used to several different nonlinear systems, and the simulation results indicated that the presented method is feasible and reasonable. Copyright © 2014 IFSA Publishing, S. L.