...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on circuits and systems . I , Regular papers >Empirical frequency-domain optimal parameter estimate for black-box processes
【24h】

Empirical frequency-domain optimal parameter estimate for black-box processes

机译：黑盒过程的经验频域最优参数估计

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

Most of the previous signal processing identification results have been achieved using either time-domain or frequency-domain algorithms. In this study, the two methods were combined to create a novel identification algorithm called the empirical frequency-domain optimal parameter (EFOP) estimate and the recursive EFOP algorithm for common linear stochastic systems disturbed with noise. A general prediction error criterion was introduced in the time-domain estimate. By minimizing the frequency-domain estimate, some general prediction error criteria were constructed for Black-box models. Then, the parameter estimation was obtained by minimizing the general prediction error criterion. This method theoretically provides the globally optimum frequency-domain estimate of the model. It has advantages in anti-disturbance performance, and can precisely identify a model with fewer sample numbers. Lastly, some simulations were carried out to demonstrate the validity of the new method.
机译：先前的大多数信号处理识别结果都是使用时域或频域算法实现的。在这项研究中，这两种方法相结合，创建了一种新颖的识别算法，称为经验频域最优参数（EFOP）估计和递归EFOP算法，用于受噪声干扰的常见线性随机系统。在时域估计中引入了一般的预测误差准则。通过最小化频域估计，为黑盒模型构建了一些通用的预测误差标准。然后，通过最小化一般预测误差准则来获得参数估计。该方法理论上提供了模型的全局最优频域估计。它在抗干扰性能方面具有优势，并且可以以更少的样本数量精确识别模型。最后，通过仿真实验证明了该方法的有效性。

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