自适应免疫克隆选择算法的参数识别

Parameter Estimation Using an Adaptive Immune Clone Selection Algorithm

提出了一种被称为是自适应免疫克隆选择算法的新型人工免疫算法,此方法可进行系统的参数识别,以解决结构的多目标优化问题.此种算法将二阶响应、适应性变异准则和疫苗因子这三种算子都引入到遗传克隆选择算法中,提高了运算的收敛速度及全局优化搜索能力.对动力系统参数识别的模拟识别结果证明了本文所提出算法的有效性与可行性.

A novel Artificial Immune Algorithm,namely Adaptive Immune Clone Selection Algorithm is proposed in this paper for parameter estimation which can be formulated as a multi-modal optimization problem with high dimension.In this method the secondary response,adaptive mutation regulation and vaccination operator are introduced in the generic Clone Selection Algorithm to improve the convergence speed and global optimum searching ability.Simulation results for identifying the parameters of a dynamic system are presented to demonstrate the effectiveness of the proposed method.