TY - GEN
T1 - Online functional prediction for spatio-temporal systems using time-varying radial basis function networks
AU - Su, J.
AU - Dodd, T. J.
N1 - Funding Information:
This research was financially supported by the Netherlands Agency for Energy and the Environment (NOVEM).
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Functional auto-regressive
KW - Kalman filter
KW - Radial basis function
UR - http://www.scopus.com/inward/record.url?scp=77953068911&partnerID=8YFLogxK
U2 - 10.1109/CAR.2010.5456749
DO - 10.1109/CAR.2010.5456749
M3 - Conference Proceeding
AN - SCOPUS:77953068911
SN - 9781424451937
T3 - CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics
SP - 147
EP - 150
BT - CAR 2010 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics
T2 - 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, CAR 2010
Y2 - 6 March 2010 through 7 March 2010
ER -