.png){kind=icon}
返回
IIR system identification using cat swarm optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2011.04.054.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
System identification
IIR system
Cat swarm optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
.png){kind=icon}
A new design method based on artificial bee colony algorithm for digital IIR filters基于人工蜂群算法的数字IIR滤波器设计新方法
PREAI
分享收藏
查看更多内容