D. San-Martín

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In this paper we present a unified framework for extreme learning machines and reservoir computing (echo state networks), which can be physically implemented using a single nonlinear neuron subject to delayed feedback. The reservoir is built within the delay-line, employing a number of "virtual" neurons. These virtual neurons receive random projections from(More)
A physical scheme based on a single nonlinear dynamical system with delayed feedback has been recently proposed for Reservoir Computing (RC) [1]. In this paper we present a computational implementation of this idea using a simple chain topology with properties derived from its physical counterpart (e.g. the reservoir is defined by two tunable parameters(More)
In this study we analyzed the performance of 12 state-of-the-art global climate models (GCMs) from 2 different model generations used in the ENSEMBLES project (a European Commissionfunded climate-change research project) over southwestern Europe. For this purpose, we assessed the similarity of the simulated and quasi-observed (reanalysis) probability(More)
Weather forecast is a complex multi-disciplinary problem which requires a cascade of different scientific tools, from differential equation solvers to high-dimensional statistical and data-mining algorithms. The demand for high-resolution predictions is continuously increasing due to the multiple applications in hydrology, agronomy, etc., which require(More)
This paper presents CalHidra 3.0, a new software package developed for dynamic simulation of water quality in rivers. CalHidra 3.0 combines a 1-D hydrodynamic model based on Saint Venant equations, a transport sub-model that incorporates the advectionedispersion terms, and a simplified version of the River Water Quality Model 1 (RWQM1) for the biochemical(More)
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