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Title: | Delay-based reservoir computing: Noise effects in a combined analog and digital implementation |
Authors: | Soriano, Miguel C. ; Ortín González, Silvia ![]() ![]() ![]() |
Issue Date: | Feb-2015 |
Publisher: | Institute of Electrical and Electronics Engineers |
Citation: | IEEE Transactions on Neural Networks and Learning Systems 26(2): 388-393 (2015) |
Abstract: | 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. |
Publisher version (URL): | http://dx.doi.org/10.1109/TNNLS.2014.2311855 |
URI: | http://hdl.handle.net/10261/133728 |
DOI: | 10.1109/TNNLS.2014.2311855 |
Identifiers: | issn: 2162-2388 |
Appears in Collections: | (IFCA) Artículos (IFISC) Artículos |
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