Nonlinear Memory Capacity of Parallel Time-Delay Reservoir Computers in the Processing of Multidimensional Signals.
Fiche publication
Date publication
juillet 2016
Journal
Neural computation
Auteurs
Membres identifiés du Cancéropôle Est :
Dr LARGER Laurent, Mme HENRIQUES Julie
Tous les auteurs :
Grigoryeva L, Henriques J, Larger L, Ortega JP
Lien Pubmed
Résumé
This letter addresses the reservoir design problem in the context of delay-based reservoir computers for multidimensional input signals, parallel architectures, and real-time multitasking. First, an approximating reservoir model is presented in those frameworks that provides an explicit functional link between the reservoir architecture and its performance in the execution of a specific task. Second, the inference properties of the ridge regression estimator in the multivariate context are used to assess the impact of finite sample training on the decrease of the reservoir capacity. Finally, an empirical study is conducted that shows the adequacy of the theoretical results with the empirical performances exhibited by various reservoir architectures in the execution of several nonlinear tasks with multidimensional inputs. Our results confirm the robustness properties of the parallel reservoir architecture with respect to task misspecification and parameter choice already documented in the literature.
Référence
Neural Comput. 2016 Jul;28(7):1411-51