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 spatiotemporal evolutionary behaviour, is investigated to demonstrate the application of the proposed modeling and learning approaches.
|Publication status||Published - 22 Jul 2015|
|Event||The 10th International Conference on Computer Science & Education - Cambridge|
Duration: 22 Jul 2015 → …
|Conference||The 10th International Conference on Computer Science & Education|
|Period||22/07/15 → …|