基于径向基函数神经网络的直流电机驱动系统模型辨识

2011 IEEE Symposium on Industrial Electronics and Applications Pub Date : 2011-12-22 DOI:10.1109/ISIEA.2011.6108685

I. Yassin, M. Taib, M. A. Abdul Aziz, N. Abdul Rahim, N. Tahir, A. Johari

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引用次数: 6

摘要

在本文中，我们提出了一个基于径向基函数神经网络(RBFNN)的非线性自回归模型(NARX)的直流电机驱动控制器模型(Rahim, 2004)。通过测试来衡量模型的准确性(使用领先一步(OSA))及其有效性(使用相关测试和直方图分析)。所得到的模型在训练集和测试集上产生的均方误差(MSE)分别为8.53 × 10−3和8.82 × 10−3，同时执行了所有验证测试。

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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Identification of DC motor drive system model using Radial Basis Function (RBF) Neural Network

In this paper, we present a Radial Basis Function Neural Network (RBFNN)-based Nonlinear Auto-Regressive Model with Exegeneous Inputs (NARX) model of a DC motor drive controller model by (Rahim, 2004). Tests were conducted to measure the accuracy of the model (using One Step Ahead (OSA) and its validity (using correlation tests and histogram analysis). The resulting model produced Mean Square Error (MSE) of 8.53 × 10−3 and 8.82 × 10−3 on the training set and test set, respectively, while fulfilling all validation tests performed.

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2011 IEEE Symposium on Industrial Electronics and Applications

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