The paper proposes to deal with noisy, sparse or short training data sequences by adding domain knowledge to the learning process of Echo State Networks (ESNs). Known constraints like monotony in the output, periodicity or bounds on output values are encoded as inequality constraints on the output weights to be learned. Exploiting that the output of an ESN is linear in the weights, Quadratic Programming is then used to obtain and optimize these. The method is applied to the prediction of pixel values from monthly, noisy satellite images of a short history of five years, thereby enabling the cleaning of images from clouds or snow.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.