• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mechanical systems and signal processing >Comparison of least squares and exponential sine sweep methods for Parallel Hammerstein Models estimation
【24h】

Comparison of least squares and exponential sine sweep methods for Parallel Hammerstein Models estimation

机译：Hammerstein模型并行估计的最小二乘法和指数正弦扫描方法的比较

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Linearity is a common assumption for many real-life systems, but in many cases the nonlinear behavior of systems cannot be ignored and must be modeled and estimated. Among the various existing classes of nonlinear models, Parallel Hammerstein Models (PHM) are interesting as they are at the same time easy to interpret as well as to estimate. One way to estimate PHM relies on the fact that the estimation problem is linear in the parameters and thus that classical least squares (LS) estimation algorithms can be used. In that area, this article introduces a regularized LS estimation algorithm inspired on some of the recently developed regularized impulse response estimation techniques. Another mean to estimate PHM consists in using parametric or non-parametric exponential sine sweeps (ESS) based methods. These methods (LS and ESS) are founded on radically different mathematical backgrounds but are expected to tackle the same issue. A methodology is proposed here to compare them with respect to (i) their accuracy, (ii) their computational cost, and (iii) their robustness to noise. Tests are performed on simulated systems for several values of methods respective parameters and of signal to noise ratio. Results show that, for a given set of data points, the ESS method is less demanding in computational resources than the LS method but that it is also less accurate. Furthermore, the LS method needs parameters to be set in advance whereas the ESS method is not subject to conditioning issues and can be fully non-parametric. In summary, for a given set of data points, ESS method can provide a first, automatic, and quick overview of a nonlinear system than can guide more computationally demanding and precise methods, such as the regularized LS one proposed here.
机译：线性是许多现实生活中系统的常见假设，但是在许多情况下，系统的非线性行为无法忽略，必须进行建模和估算。在各种现有的非线性模型类别中，并行Hammerstein模型（PHM）很有趣，因为它们同时易于解释和估计。估计PHM的一种方法依赖于以下事实：估计问题在参数上是线性的，因此可以使用经典的最小二乘（LS）估计算法。在该领域，本文介绍了一种正则化的LS估计算法，该算法的灵感来自一些最近开发的正则化的脉冲响应估计技术。估计PHM的另一个方法是使用基于参数或非参数的指数正弦扫描（ESS）方法。这些方法（LS和ESS）基于根本不同的数学背景，但有望解决相同的问题。这里提出一种方法来比较它们（i）其准确性，（ii）其计算成本和（iii）其抗噪声能力。在模拟系统上针对方法的各个参数和信噪比的几个值进行测试。结果表明，对于给定的数据点集，ESS方法对计算资源的要求低于LS方法，但准确性也较低。此外，LS方法需要预先设置参数，而ESS方法不受条件问题的影响，并且可以完全非参数化。总而言之，对于给定的一组数据点，ESS方法可以提供非线性系统的第一，自动，快速概述，而不是可以指导更多对计算要求更高且更精确的方法，例如此处提出的正则化LS。

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