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Online functional prediction for spatio-temporal systems using a generalized time-varying Radial Basis Function networks framework

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2 Author(s)
Jionglong Su ; Department of Automatic Control and Systems Engineering, The University of Sheffield, S1 3JD, UK ; T. J. Dodd

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 in a generalized Vector Auto-Regressive (VAR) framework 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.

Published in:

Industrial Engineering and Engineering Management (IE&EM), 2010 IEEE 17Th International Conference on

Date of Conference:

29-31 Oct. 2010