一种用于输入-系统-状态辨识的 Kullback-Leibler 散度方法
A Kullback-Leibler divergence method for input-system-state identification
AI总结：
本文提出一种基于 Kullback-Leibler 散度的 Kalman filter 框架方法，通过比较先验与后验分布选择最合理的输入-参数-状态估计方案，有效解决初始参数猜测导致的不确定性问题，为系统监测提供强大工具。
摘要：
本文在 Kalman filter 框架内检验了一种新型 Kullback-Leibler 散度方法的能力，以选择结果最合理的输入-参数-状态估计执行方案。该辨识面临因不同初始参数集猜测产生不同结果而带来的不确定性，所检验的方法利用数据中从先验分布到后验分布所获得的信息来解决此问题。首先，对多个不同的初始参数集执行 Kalman filter，提供系统输入-参数-状态估计。其次，使用 Kullback-Leibler 散度将所得的后验分布与初始先验分布同时进行比较。最后，选择具有最小 Kullback-Leibler 散度的辨识作为结果最合理的方案。重要的是，该方法在线性、非线性和有限信息应用中均被证明能选出表现更好的辨识，为系统监测提供了强大工具。
英文摘要：
The capability of a novel Kullback-Leibler divergence method is examined herein within the Kalman filter framework to select the input-parameter-state estimation execution with the most plausible results. This identification suffers from the uncertainty related to obtaining different results from different initial parameter set guesses, and the examined approach uses the information gained from the data in going from the prior to the posterior distribution to address the issue. Firstly, the Kalman filter is performed for a number of different initial parameter sets providing the system input-parameter-state estimation. Secondly, the resulting posterior distributions are compared simultaneously to the initial prior distributions using the Kullback-Leibler divergence. Finally, the identification with the least Kullback-Leibler divergence is selected as the one with the most plausible results. Importantly, the method is shown to select the better performed identification in linear, nonlinear, and limited information applications, providing a powerful tool for system monitoring.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
