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Data assimilation transfers information from an observed system to a physically based model system with state variables x(t). The observations are typically noisy, the model has errors, and the initial state x(t 0) is uncertain: the data assimilation is statistical. One can ask about expected values of functions G(X) on the path X = {x(t 0),. . ., x(t m)}(More)
We examine the use of synchronization as a mechanism for extracting parameter and state information from experimental systems. We focus on important aspects of this problem that have received little attention previously and we explore them using experiments and simulations with the chaotic Colpitts oscillator as an example system. We explore the impact of(More)
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