首页> 外文期刊>IFAC PapersOnLine >Identification of nonlinear dynamical system with synthetic data: a preliminary investigation
【24h】

Identification of nonlinear dynamical system with synthetic data: a preliminary investigation

机译:用合成数据识别非线性动力系统:初步调查

获取原文
           

摘要

This paper introduces a new rationale for learning nonlinear dynamical systems. The method makes use of an additional identification dataset, obtained without performing a new experiment on the system under study. The data are generated in an automatical manner, starting from a set of experimentally acquired measurements. In order to leverage the additional generated information, fundamental techniques from the machine learning field known as Semi-Supervised Learning (SSL) are employed and adapted. The problem is then cast as a regularized parametric learning problem. The effectiveness of the proposed approach is assessed on various nonlinear benchmark systems via repeated simulations, comparing the obtained results with a standard regularization method for learning parametric models.
机译:本文介绍了学习非线性动力系统的新理由。该方法利用另外的识别数据集,而不在没有对研究中的系统进行新实验的情况下获得。从一组实验获取的测量开始,以自动方式生成数据。为了利用额外的生成信息,采用和调整所谓的机器学习(SSL)的机器学习领域的基本技术。然后将问题作为正则化参数学学习问题。通过重复模拟对各种非线性基准系统评估了所提出的方法的有效性,将获得的结果与用于学习参数模型的标准正则化方法进行比较。

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