...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of lifecycle performance engineering >An efficient method for Bayesian system identification based on Markov chain Monte Carlo simulation
【24h】

An efficient method for Bayesian system identification based on Markov chain Monte Carlo simulation

机译：基于马尔可夫链蒙特卡洛模拟的贝叶斯系统辨识有效方法

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

This paper proposes an efficient method for identifying a dynamic system using measured accelerations. A practical mathematical model of a dynamic system is developed based on modal superposition for response prediction. To explicitly address uncertainties, system identification is treated as a Bayesian inference problem where the objective is to identify the posterior PDF conditional measured data. Unless a very simple system is considered, the posterior PDF is usually complicated in the sense that its significant region is concentrated in the neighbourhood of an extended and extremely complex manifold. An effective Markov chain Monte Carlo algorithm is developed to sample from the posterior PDF. Given the generated samples, a framework is proposed to systematically consider multiple models whose relative plausibility is quantified by the weightings depending on the PDF values of the samples. It is illustrated that the proposed method can handle both globally identifiable and unidentifiable problems.
机译：本文提出了一种使用测得的加速度识别动态系统的有效方法。基于模态叠加为响应预测开发了一个动态系统的实用数学模型。为了明确解决不确定性，系统识别被视为贝叶斯推理问题，其目的是识别后PDF条件测量数据。除非考虑使用非常简单的系统，否则后PDF通常会很复杂，因为后PDF的显着区域集中在扩展且极其复杂的流形附近。开发了一种有效的马尔可夫链蒙特卡罗算法来从后PDF中进行采样。给定生成的样本，提出了一个框架来系统地考虑多个模型，这些模型的相对合理性通过取决于样本PDF值的权重进行量化。说明了所提出的方法可以处理全局可识别和不可识别的问题。

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