基于狂猫群优化的系统辨识

Archana Sarangi, Shubhendu Kumar Sarangi, Madhurima Mukherjee, S. Panigrahi
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引用次数: 9

摘要

传统的基于导数的自适应滤波和系统辨识算法在应用于无限脉冲响应(IIR)系统时会产生稳定性问题。本文将IIR系统的辨识作为一项优化任务。通过引入疯狂度的概念,对猫群优化算法进行了改进,产生了疯狂猫群优化算法(Crazy- cso)。利用改进后的算法找到了更好的解。通过对几个标准IIR系统的仿真研究,验证了改进算法的有效性。与基于粒子群优化(PSO)和基于猫群优化(CSO)的识别相比,该方法具有更好的识别性能,提供了更好的输出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
System identification by Crazy-cat swarm optimization
Adaptive filtering and system identification by traditional derivative based algorithms create stability issues when used in infinite impulse response (IIR) systems. In this paper, the identification of IIR system is used as an optimization task. A modification is approached to cat swarm optimization by introducing the concept of craziness to produce Crazy cat swarm optimization(Crazy-CSO) algorithm. The new modified version of the algorithm has been utilized to find a better solution. The efficiency of the modified algorithm is verified by identification of few standard IIR systems through simulation study. The new method exhibits finer identification performance as compared to particle swarm optimization (PSO) and cat swarm optimization (CSO) based identification by providing superior outputs.
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