• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IFAC PapersOnLine >Nonlinear System Identification with Dominating Output Noise - A Case Study on the Silverbox
【24h】

Nonlinear System Identification with Dominating Output Noise - A Case Study on the Silverbox

机译：带支配输出噪声的非线性系统识别 - Silverbox的案例研究

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In the overview paper on nonlinear system identification Schoukens and Ljung (2019), it was indicated that reliable expressions to calculate the variance of an estimated nonlinear model are lacking, especially if the disturbing noise is entering the nonlinear regressors. In this study, we provide a better view on the driving mechanisms of the variability of estimated nonlinear models that is due to noise on the output. To do so, we follow a double approach. Firstly, a basic insight on the impact of disturbing noise on the monomial ynis studied. Next, these insights are used in a case study on the forced Duffing oscilator data, also called the Silver box (Schoukens and No?l, 2016). The following models are studied: Nonlinear autoregressive exogenous models (NARX) using a 2-layer Neural Net (NARX-NN) and a polynomial (NARX-poly) expansion; and polynomial nonlinear state space models (PNLSS). This limited study indicates that an output error criterion (PNLSS, and NARX used in a simulation mode) does better than minimizing the equation error (NARX-NN and NARX-poly in prediction mode). When the signal-to-noise ratio (SNR) drops below 20 dB, the reduction in the error is more than a factor 10. This is a strong indication that, just as for linear identification, it is very important to properly deal with the noise properties in the cost function whenever the SNR of the output measurements drops such that noise becomes more important than structural model errors.
机译：在非线性系统识别Schoukens和Ljung（2019）的概述文件中，表明缺乏可靠的表达式，以计算估计的非线性模型的变化，特别是如果干扰噪声进入非线性回归。在这项研究中，我们对估计的非线性模型的可变性的驱动机制提供了更好的观点，这是由于输出噪声噪声。为此，我们遵循双重方法。首先，对扰动噪声对单体YnIs的影响的基本洞察。接下来，这些见解用于对强制Duffing辅助器数据的案例研究，也称为银箱（Schoukens和No？L，2016）。研究了以下模型：使用2层神经网络（NARX-NN）和多项式（NARX-POLY）膨胀的非线性自回归性外源模型（NARX）;和多项式非线性状态空间模型（PNLS）。该有限研究表明，在模拟模式中使用的输出误差标准（PNLS和NARX）比最小化方程误差（NARX-NN和NARX-POLY在预测模式中）更好地实现。当信噪比（SNR）降低到20dB以下时，误差的减少超过一个因素10.这是一个强大的指示，即线性识别，正确处理的是非常重要的每当输出测量的SNR下降时，成本函数中的噪声属性会降低噪声比结构模型错误更重要。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号