基于禁忌搜索算法的系统辨识

System identification based on tabu search

本文研究了禁忌搜索算法解决系统辨识问题的可行性。首先将系统辨识问题转化为参数空间上的优化问题,然后利用禁忌搜索算法求取优化问题的最优解以获得系统参数的最优估计。分别通过对离散和连续系统的仿真研究,表明了本方法的可行性。并与其它方法进行了比较,结果表明本方法在搜索全局最优和克服噪音上都具有很好的效果。

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.