基于自适应蚁群优化的Volterra核辨识算法研究

摘要

A Volterra series identification method based on adaptive ant colony optimization ( AACO) algorithm was proposed. With the proposed method, Volterra kernel was identified using ant colony optimization algorithm, the parameters of the ant colony algorithm could be adaptively adjusted with increase in evolution number. At the same time, the proposed method was compared with Volterra kernel identification method based on basic ant colony optimization (ACO). The simulation results showed that the proposed method and ACO identification one have good identification accuracy, convergence stability and robust anti-noise performance whether in noise-free or noise environment; however, the convergence speed of the proposed method is faster than that of ACO identification one.%提出了一种基于自适应蚁群优化(AACO)的Volterra核辨识方法.该方法将蚁群算法应用于Volterra时域核的辨识,并能够随着进化次数的增加,自适应调整基本蚁群算法的参数.同时,与相应的基于蚁群优化(ACO)的Volterra 核辨识方法进行了对比分析.仿真结果表明,提出的方法与蚁群优化辨识方法不论在无噪声环境下,还是在有噪声干扰下,都能得到很好的辨识精度、收敛稳定性和较强的鲁棒抗噪性能,然而,在收敛速度方面,提出的方法优于蚁群优化辨识方法.