System identification based on tabu search

The method using tabu search to identify system models is developed in this paper. By converting the system identification problem into an optimization problem in parameter space, the tabu search is used to seek for the global optimal solution as the optimal estimation of the parameters. Simulations on both the discrete and continuous systems are conducted to verify the feasibility. Compared with results obtained from genetic algorithm, the performance of the tabu search is found to be better in approaching the global optima. It is also proved that the method is effective in dealing with noises.