自适应遗传算法的改进及在系统辨识中应用研究

Improved Adaptive Genetic Algorithm and its Application Research in Parameter Identification

为解决传统遗传算法早熟及收敛速度慢的问题,提出了一种改进的自适应遗传算法。通过对一典型的大海捞针类(NiH)问题的试验,证明了改进后的遗传算法在全局优化和快速收敛能力上有较大的提高。在此基础上将该算法应用于系统参数辨识中,辨识结果表明该方法具有参数辨识精度高,抗噪声能力大,对输入信号通用性强,也适用于非线性系统参数辨识的优点,具有重要的工程使用价值。

An improved adaptive genetic algorithm （IAGA） was proposed to avoid the premature problem and the slow convergence, Through the experiment of a typical Needle-in-a-haystack problem, the proposed algorithm shows its better global optimal ability and its faster convergence ability. Based on the above, the improved algorithm was applied to identify system parameter. The identification results show that this method has the advantages of high parameter identification precision, strong ability of resistance to the noise, good input signal generality and identification of the nonlinear system, so it has important practical values.