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首页> 外文期刊>The Open Electrical & Electronic Engineering Journal >An Internal Model Controller for Three-phase APF Based on LS-Extreme Learning Machine
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An Internal Model Controller for Three-phase APF Based on LS-Extreme Learning Machine

机译：基于LS极限学习机的三相APF内模控制器。

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Aiming at the problem that the three-phase APF’s dynamic model is a multi-variable, nonlinear and strongcoupling system, an internal model controller for three-phase APF based on LS-Extreme Learning Machine is studied inthis paper. As a novel single hidden layer feed-forward neural networks, extreme learning machine (ELM) has several advantages:simple net structural, fast learning speed, good generalization performance and so on. In order to improve thecontroller’s dynamic responses, a least squares extreme learning machine for internal model control is proposed. A leastsquares ELM regression(LS-ELMR) model for the three-phase APFS on-line monitoring was built from external factorswith in-out datum. Moreover, the relative stable error is presented to evaluate the system performance and the features forthe internal model control system based on extreme learning machine, neural network, kernel ridge regress and supportvector machine. The experimental results show that the LS-internal model control system based on extreme learning machinehas good dynamic performance and strong filtering result.
机译：针对三相APF的动力学模型是一个多变量，非线性，强耦合的问题，本文研究了一种基于LS-Extreme Learning Machine的三相APF的内模控制器。作为一种新颖的单隐藏层前馈神经网络，极限学习机（ELM）具有以下优点：网络结构简单，学习速度快，泛化性能好等。为了改善控制器的动态响应，提出了一种用于内部模型控制的最小二乘极限学习机。利用含进出基准的外部因素建立了三相APFS在线监测的最小二乘ELM回归模型。此外，提出了一个相对稳定误差，用于评价基于极限学习机，神经网络，核岭回归和支持向量机的内模控制系统的性能和性能。实验结果表明，基于极限学习机的LS内部模型控制系统具有良好的动态性能和较强的滤波效果。

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