Accurate state and parameter estimation in nonlinear systems with sparse observations
Accurate state and parameter estimation in nonlinear systems with sparse observations
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DOI：
10.1016/j.physleta.2014.01.027
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发表时间：
2014-02-21
影响因子：
2.6
通讯作者：
Parlitz, Ulrich
中科院分区：
文献类型：
--
作者：
Rey, Daniel;Eldridge, Michael;Parlitz, Ulrich
当任何观测时间的观测数量不足时，将信息从观测转移到复杂系统的模型可能会遇到障碍。当表达混乱行为时尤其如此。我们将展示如何使用时间延迟嵌入，熟悉的非线性动力学，提供所需的信息，以获得准确的状态和参数估计。对参数和未观测状态的良好估计对于模型系统的未来状态的良好预测是必要的。这种方法可能是至关重要的，在允许复杂的系统，如神经系统和天气预测，其中不充分的测量是典型的预测的理解。(C)2014爱思唯尔有限公司版权所有。
Transferring information from observations to models of complex systems may meet impediments when the number of observations at any observation time is not sufficient. This is especially so when chaotic behavior is expressed. We show how to use time-delay embedding, familiar from nonlinear dynamics, to provide the information required to obtain accurate state and parameter estimates. Good estimates of parameters and unobserved states are necessary for good predictions of the future state of a model system. This method may be critical in allowing the understanding of prediction in complex systems as varied as nervous systems and weather prediction where insufficient measurements are typical. (C) 2014 Elsevier B.V. All rights reserved.