Sauer Timothy D, Schiff Steven J
Department of Mathematical Sciences, George Mason University, Fairfax, Virginia 22030, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2009 May;79(5 Pt 1):051909. doi: 10.1103/PhysRevE.79.051909. Epub 2009 May 13.
Data assimilation in dynamical networks is intrinsically challenging. A method is introduced for the tracking of heterogeneous networks of oscillators or excitable cells in a nonstationary environment, using a homogeneous model network to expedite the accurate reconstruction of parameters and unobserved variables. An implementation using ensemble Kalman filtering to track the states of the heterogeneous network is demonstrated on simulated data and applied to a mammalian brain network experiment. The approach has broad applicability for the prediction and control of biological and physical networks.
动态网络中的数据同化本质上具有挑战性。本文介绍了一种方法，用于在非平稳环境中跟踪振荡器或可兴奋细胞的异构网络，使用均匀模型网络来加速参数和未观测变量的精确重建。在模拟数据上展示了一种使用集合卡尔曼滤波来跟踪异构网络状态的实现方法，并将其应用于哺乳动物脑网络实验。该方法在生物和物理网络的预测和控制方面具有广泛的适用性。
