...
  • 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 >Functionally Pooled models for the global identification of stochastic systems under different pseudo-static operating conditions
【24h】

Functionally Pooled models for the global identification of stochastic systems under different pseudo-static operating conditions

机译：在不同的伪静态操作条件下全局识别随机系统的功能汇总模型

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

The problem of identifying a single global model for stochastic dynamical systems operating under different conditions is considered within a novel Functionally Pooled (FP) identification framework. Within it a specific value of a measurable scheduling variable characterizes each operating condition that has pseudo-static effects on the dynamics. The FP framework incorporates parsimonious FP models capable of fully accounting for cross correlations among the operating conditions, functional pooling for the simultaneous treatment of all data records, and statistically optimal estimation. Unlike seemingly related Linear Parameter Varying (LPV) model identification leading to suboptimal accuracy in this context, the postulated FP model estimators are shown to achieve optimal statistical accuracy. An application case study based on a simulated railway vehicle under various mass loading conditions serves to illustrate the high achievable accuracy of FP modelling and the improvements over local models employed within LPV-type identification.
机译：在新颖的功能池（FP）识别框架中，考虑了为在不同条件下运行的随机动力系统识别单个全局模型的问题。在其中可测量的调度变量的特定值表征了对动态有伪静态影响的每个运行条件。 FP框架合并了简约的FP模型，这些模型能够完全考虑操作条件之间的相互关系，用于同时处理所有数据记录的功能池以及统计上的最佳估计。在这种情况下，与看似相关的线性参数变量（LPV）模型识别导致次优准确性不同，假定的FP模型估计器可实现最佳的统计准确性。基于模拟铁路车辆在各种质量负载条件下的应用案例研究旨在说明FP建模可实现的高准确性以及对LPV类型识别中使用的局部模型的改进。

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