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This technical note considers the identification of nonlinear discrete-time systems with additive process noise but without measurement noise. In particular, we propose a method and its associated algorithm to identify the system nonlinear functional forms and their associated parameters from a limited number of time-series data points. For this, we cast this identification problem as a sparse linear...
Causal networks are essential in many applications to illustrate causal relations in dynamical systems. In a view of statistics, Granger Causality (GC) gives a definition for causal ordering of time series, which implies a parametric model for stationary processes. In a systematic view, Dynamical Structure Function (DSF) is proposed to provide a general parametric representation for linear causal...
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