基于改进遗传算法的系统参数辨识

传统的遗传算法在解决具体优化问题时存在着收敛速度慢和容易早熟的缺点。针对系统参数辨识,提出了一种改进的遗传算法。通过合理选择复制策略、改进适应度函数计算方法,克服了早熟现象,保证了种群的多样性,避免了后期适应值接近而导致收敛速度过慢。通过该算法对典型二阶系统的传递函数进行参数求解,在信噪比较大的情况下,得到几乎无偏的估计。实验结果证明了该方法的有效性。

System Parameter Identification based on Improved Genetic Algorithm

Traditional genetic algorithms have convergence speed and premature easily in solving specific optimization problems, in case of system parameters identification, this paper presents an improved genetic algorithm. By selecting reasonable replication strategy, and improving the fitness function calculation, overcomes the prematurity, ensures the diversity of the population, avoid slow convergence which is resulted by the close fitness value in the later time. Solving the parameters of typical second-order system transfer function by the algorithm, in the case of larger SNR, obtained the estimation to be almost no bias. Experimental results showed the effectiveness of the method.