2015/10/13 by Lyudmila Grigoryeva, Grigoryeva, Lyudmila, Julie Henriques +6
Computer Science · Engineering · #Advanced Memory and Neural Computing #FOS: Computer and information sciences #Neural Networks and Applications #Neural Networks and Reservoir Computing #Neural and Evolutionary Computing (cs.NE) #cs.NE
paper · pdf · doi:10.48550/arxiv.1510.03891
24 pages, 6 figures
arxiv created 2015/10/13 · openalex publication_date 2015/10/13 · arxiv updated 2015/10/15 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
This paper 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 parameters and architecture and its performance in the execution of a specific task. Second, the inference properties of the ridge regression estimator in the multivariate context is 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 that had already been documented in the literature.