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Título: | Delay-based reservoir computing: Noise effects in a combined analog and digital implementation |
Autor: | Soriano, Miguel C. ; Ortín González, Silvia CSIC ORCID; Keuninckx, Lars; Appeltant, Lennert; Danckaert, Jan; Pesquera, Luis CSIC ORCID ; Van der Sande, Guy CSIC ORCID | Fecha de publicación: | feb-2015 | Editor: | Institute of Electrical and Electronics Engineers | Citación: | IEEE Transactions on Neural Networks and Learning Systems 26(2): 388-393 (2015) | Resumen: | Reservoir computing is a paradigm in machine learning whose processing capabilities rely on the dynamical behavior of recurrent neural networks. We present a mixed analog and digital implementation of this concept with a nonlinear analog electronic circuit as a main computational unit. In our approach, the reservoir network can be replaced by a single nonlinear element with delay via time-multiplexing. We analyze the influence of noise on the performance of the system for two benchmark tasks: 1) a classification problem and 2) a chaotic time-series prediction task. Special attention is given to the role of quantization noise, which is studied by varying the resolution in the conversion interface between the analog and digital worlds. | Versión del editor: | http://dx.doi.org/10.1109/TNNLS.2014.2311855 | URI: | http://hdl.handle.net/10261/133728 | DOI: | 10.1109/TNNLS.2014.2311855 | Identificadores: | issn: 2162-2388 |
Aparece en las colecciones: | (IFCA) Artículos (IFISC) Artículos |
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