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System identification by Crazy-cat swarm optimization

机译:疯狂猫群优化的系统识别

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摘要

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.
机译:基于传统的基于衍生算法的自适应滤波和系统识别在无限脉冲响应(IIR)系统中使用时创建稳定性问题。在本文中,使用IIR系统的识别作为优化任务。通过引入疯狂的概念来产生疯狂猫群优化(Craft-CSO)算法来实现修改。已经利用了算法的新修改版本来找到更好的解决方案。通过仿真研究识别少数标准IIR系统来验证修改算法的效率。与粒子群优化(PSO)和CAT群优化(CSO)基于卓越的输出,新方法表现出更精细的识别性能。

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