基于变结构模糊神经网络的开关磁阻电机建模

ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501) Pub Date : 2001-08-18 DOI:10.1109/ICEMS.2001.971908

Zheng Hongtao, Qiao Bin, Guo Zhijiang, Jian Jingping

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引用次数: 2

摘要

开关磁阻电动机(SRM)在很大的工作范围内，几乎总是在饱和区域内工作。这产生了非常强的非线性，这使得很难推导出机器行为的综合数学模型。提出了SRM的变结构模糊神经网络模型。在Takagi-Sugeno模糊神经网络的基础上，提出了一种变结构步进学习算法。模糊仿真结果表明，该方法比BP神经网络模型具有更高的收敛精度和更短的收敛时间。

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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Modelling of switched reluctance motor based on variable structure fuzzy-neural networks

Switched reluctance motors (SRM) are almost always operated within the saturation region for a very large operation region. This yields very strong nonlinearities, which makes it very difficult to derive a comprehensive mathematical model for the behavior of the machine. This paper presents the variable structure fuzzy-neural networks model of SRM. Based on the Takagi-Sugeno fuzzy-neural networks, a variable structure and step learning arithmetic was presented. Then the fuzzy-simulation results show that this method is more precise and less time-consuming for convergence than BP neural networks model.

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ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)

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