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- W2030023668 abstract "In this paper, functional prediction is carried out for spatio-temporal systems in which the spatial data is irregularly sampled. We propose a novel method called Kalman Filter Radial Basis Function (KF-RBF) for such a purpose. It casts the problem into a Reproducing Kernel Hilbert Space (RKHS) defined by some continuous, symmetric and positive definite Radial Basis Function (RBF), thereby allowing for irregular sampling in the spatial domain. A Functional Auto-Regressive (FAR) model describing the system evolution in the temporal domain is further assumed. The FAR model is then formulated as a Vector Auto-Regressive (VAR) model embedded into a Kalman Filter (KF). This is achieved by projecting the unknown functions onto a time-invariant functional subspace. Subsequently, the weight vectors obtained become inputs into a Kalman Filter (KF). In this way, nonstationary functions can be forecasted by evolving these weight vectors." @default.
- W2030023668 created "2016-06-24" @default.
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- W2030023668 date "2010-03-01" @default.
- W2030023668 modified "2023-09-23" @default.
- W2030023668 title "Online functional prediction for spatio-temporal systems using time-varying Radial Basis Function networks" @default.
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- W2030023668 doi "https://doi.org/10.1109/car.2010.5456749" @default.
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