Multirate systems are abundant in industry; for example, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. The study of multirate syst...Multirate systems are abundant in industry; for example, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. The study of multirate systems goes back to the early 1950's, and has become an active research area in systems and control. This paper briefly surveys the history of development in the area of multirate systems, and introduces some basic concepts and latest results on multirate systems, including a polynomial transformation technique and the lifting technique as tools for handling multirate systems, lifted state space models, parameter identification of dual-rate systems, how to determine fast single-rate models from dual-rate models and directly from dual-rate data, and a hierarchical identification method for general multirate systems. Finally, some further research topics for multirate systems are given.展开更多
针对含缺失数据的变量带误差(EIV)系统,直接利用协方差匹配(CM)算法进行辨识的精度有限,为此提出一种协方差匹配迭代(covariance matching based iterative,CMI)算法。首先基于不完整数据集,利用CM算法获得模型参数的初始估计,然后采用...针对含缺失数据的变量带误差(EIV)系统,直接利用协方差匹配(CM)算法进行辨识的精度有限,为此提出一种协方差匹配迭代(covariance matching based iterative,CMI)算法。首先基于不完整数据集,利用CM算法获得模型参数的初始估计,然后采用交互估计理论,利用获得的参数计算缺失输出数据的估计,重构得到完整的数据集后再进一步利用CM算法更新参数估计。两者执行了递阶计算过程,通过迭代辨识逐步提高参数估计精度。仿真结果表明,CMI算法的参数估计误差在输出数据缺失率达到60%时仍然能够保持在2%以下,且随输入端和输出端噪信比的变化速率仅为CM算法的16.8%和10.8%,验证了所提算法具有较高的辨识精度和良好的鲁棒性。展开更多
基金Supported by the Natural Sciences and Engineering Research Council of Canada and National Natural Science Foundation of P.R.China
文摘Multirate systems are abundant in industry; for example, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. The study of multirate systems goes back to the early 1950's, and has become an active research area in systems and control. This paper briefly surveys the history of development in the area of multirate systems, and introduces some basic concepts and latest results on multirate systems, including a polynomial transformation technique and the lifting technique as tools for handling multirate systems, lifted state space models, parameter identification of dual-rate systems, how to determine fast single-rate models from dual-rate models and directly from dual-rate data, and a hierarchical identification method for general multirate systems. Finally, some further research topics for multirate systems are given.
文摘针对含缺失数据的变量带误差(EIV)系统,直接利用协方差匹配(CM)算法进行辨识的精度有限,为此提出一种协方差匹配迭代(covariance matching based iterative,CMI)算法。首先基于不完整数据集,利用CM算法获得模型参数的初始估计,然后采用交互估计理论,利用获得的参数计算缺失输出数据的估计,重构得到完整的数据集后再进一步利用CM算法更新参数估计。两者执行了递阶计算过程,通过迭代辨识逐步提高参数估计精度。仿真结果表明,CMI算法的参数估计误差在输出数据缺失率达到60%时仍然能够保持在2%以下,且随输入端和输出端噪信比的变化速率仅为CM算法的16.8%和10.8%,验证了所提算法具有较高的辨识精度和良好的鲁棒性。
