基于自适应蚁群优化的Volterra核辨识算法研究

李志农;唐高松;肖尧先;邬冠华

振动与冲击 ›› 2011, Vol. 30 ›› Issue (10) : 35-38.

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PDF(1069 KB)
振动与冲击 ›› 2011, Vol. 30 ›› Issue (10) : 35-38.
论文

基于自适应蚁群优化的Volterra核辨识算法研究

  • 李志农1; 唐高松2; 肖尧先1; 邬冠华1
作者信息 +

Volterra series identification method based on adaptive ant colony optimization

  • Li Zhi-nong1; Tang Gao-song2; Xiao Yao-xian1; Wu Guan-hua1
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摘要

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,本文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,本文提出的方法优于蚁群优化辨识方法。

Abstract

A Volterra series identification method based on Adapt Ant Colony Optimization (AACO) algorithm is proposed. In the proposed method, the Volterra kernel is identified by ant colony optimization algorithm. the parameters of the ant colony algorithm can be adaptively adjusted as the incre.ase of evolution number. At the same time, the proposed method are compared with the Volterra kernel identification method based on basic ant colony optimization (ACO). The simulation result shows that the proposed method and ACO identification method have good identification accuracy, convergence stability and robust anti-noise performance whether in the noise-free or noise environment. However in the convergence speed, the proposed method is supirior to the ACO identification method.

关键词

自适应蚁群优化 / Volterra级数 / 非线性系统 / 系统辨识

Key words

adapt ant colony optimization (aaco) / volterra series / nonlinear system / system identification

引用本文

导出引用
李志农;唐高松;肖尧先;邬冠华. 基于自适应蚁群优化的Volterra核辨识算法研究[J]. 振动与冲击, 2011, 30(10): 35-38
Li Zhi-nong;Tang Gao-song;Xiao Yao-xian;Wu Guan-hua. Volterra series identification method based on adaptive ant colony optimization[J]. Journal of Vibration and Shock, 2011, 30(10): 35-38

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