粒子群算法在模型降阶中的应用

Mitali Vijay Kondukwar, P. Dewangan
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引用次数: 0

摘要

本文讨论了粒子群优化(PSO)技术,并将高阶模型简化为低阶模型。然后将结果与使用传统方法产生的结果进行比较。在阶跃响应规范、波体响应规范和性能指标的基础上,对该模型进行了比较,证明了该模型的优越性。与其他方法相比,该模型的主要优点是在更短的时间内提供合理的精度。此外,简化后的模型保留了原系统的时间和频率响应特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of Particle Swarm Optimization for Model Order Reduction
In this paper, Particle Swarm Optimization (PSO) technique has been discussed and reduction of the higher order model to a lower order model performed. The results are then compared to those produced using traditional methods. On the basis of step response specification, bode response specification, and performance indices, a comparison is made to demonstrate the superiority of the proposed model. The primary benefit of the proposed model is to offer reasonable accuracy in less time relative to other methods. Furthermore, the reduced model retains the time and frequency response characteristics of the original system.
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