Model term selection for spatio-temporal system identification using mutual information

A new mutual information based algorithm is introduced for term selection in spatio-temporal models. A generalised cross validation procedure is also introduced for model length determination and examples based on cellular automata, coupled map lattice and partial differential equations are described

A wavelet neural network model for spatio-temporal image processing and modeling

Spatio-temporal images are a class of complex dynamical systems that evolve over both space and time. Compared with pure temporal processes, the identification of spatio-temporal models from observed images is much more difficult and quite challenging. Starting with an assumption that there is no a priori information about the true model but only observed data are available, this work introduces a new type of wavelet network that utilizes the easy tractability and exploits the good properties of multiscale wavelet decompositions to represent the rules of the associated spatio-temporal evolutionary system. An application to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, is investigated to demonstrate the application of the proposed modeling and learning approaches.This work was supported in part by EPSRC under Grant: EP/I011056/1 and Platform Grant EP/H00453X/