基于Crow搜索算法的神经模糊模型非线性系统辨识

2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) Pub Date : 2020-12-20 DOI:10.1109/STA50679.2020.9329306

Mourad Turki, M. A. Zeddini, Issa Malloug, A. Sakly

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引用次数: 0

摘要

本文提出了一种新的优化算法——乌鸦搜索算法(Crow Search algorithm CSA)，用于求解TS型神经模糊模型。在提出的研究中，粒子由两个任务组成:它的结构和它的参数。通过对非线性系统的建模，将CSA算法与遗传算法和粒子群算法进行了比较。结果表明，与遗传算法和粒子群算法相比，CSA方法能给出最优的均值和标准差。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Nonlinear System's Identification using Neuro-Fuzzy model tuned by Crow Search Algorithm

We propose in this work a new algorithm of optimization named Crow Search Algorithm CSA to elicit neuro-fuzzy model such TS type. In the proposed study, a particle is formed by two tasks: its structure and its parameters. The CSA algorithm was compared with others: GA and PSO through a modeling of nonlinear system. The results prove that CSA method gives optimal mean of MSE and optimal of standard deviation of MSE compared to GA and PSO.

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2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

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