| 引用本文: | 吕刚,范瑜,李国国.动态驱动神经网络辨识永磁直线同步电动机模型[J].控制理论与应用,2007,24(1):99~102.[点击复制] |
| Lü Gang, FAN Yu, LI Guo-guo.Hybrid nonlinear autoregressive neuralnetworks for permanent-magnet linear synchronous motoridentification[J].Control Theory and Technology,2007,24(1):99~102.[点击复制] |
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| 动态驱动神经网络辨识永磁直线同步电动机模型 |
| Hybrid nonlinear autoregressive neuralnetworks for permanent-magnet linear synchronous motoridentification |
| 摘要点击 1437 全文点击 1273 投稿时间：2005-04-14 修订日期：2006-02-23 |
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| DOI编号 10.7641/j.issn.1000-8152.2007.1.018 |
| 2007,24(1):99-102 |
| 中文关键词 神经网络 永磁直线同步电动机 辨识 混合神经网络 NDEKF |
| 英文关键词 neural networks permanent-magnet linear
synchronous motor identification hybrid nonlinear autoregressive
neural network NDEKF |
| 基金项目 |
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| 中文摘要 |
| 永磁直线同步电动机(PMLSM)模型的建立对研究其稳态特性、动态特性和控制策略都是非常重要的.
本文利用动态驱动神经网络对其进行建模,
并在代价函数一致的基础上加入残差分析法来辨识模型的阶次,
使得神经网络具有自动识别阶次的能力.为了克服神经网络结构依靠人工试凑的不足,
使用基于Hession矩阵的修剪法来优化其结构.
考虑到改进BP算法(学习速率自适应、动量项的方法)的一些固有缺点,
使用NDEKF(基于节点的解耦扩展Kalman滤波器算法) 来训练网络.实验证明,
混合网络能够准确辨识出试验样机的阶次并且输出结果与实际结果十分接近;
同时将NDEKF与改进BP算法 进行对比,
NDEKF算法具有收敛较快、泛化能力强等特点. |
| 英文摘要 |
| The modeling of permanent-magnet
linear synchronous motor is crucial to the control, static and
dynamic characters analysis for the system. The model of
permanent-magnet linear synchronous motor is presented in this paper
by using neural networks of the nonlinear autoregressive with
exogenous inputs. For the same cost function, residual signal
analysis is employed to identify motor's order automatically. Some
shortages of back-propagation algorithm are
considered, so NDEKF (node-decoupled extended Kalman filter) is applied to train networks. Finally,
experiment results show that the hybrid neural networks of the nonlinear autoregressive
with exogenous inputs can identify object's order precisely, and the output of networks is very close
to the experimental result. In the experiment, the performance of NDEKF is often superior to that of BP,
such as it requires significantly fewer presentations of training data and shorter training time than BP does,
and has the better generalization ability. |
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