Journal
SCIENTIFIC REPORTS
Volume 5, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/srep14945
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Funding
- MINECO (Spain)
- Comunitat Autonoma de les Illes Balears
- FEDER [TEC2012-36335, FIS2012-30634]
- Grups Competitius
- Ministerio de Economa y Competitividad, Spain [TEC2012-38864-C03-03]
- FEDER funds
- Conselleria d'Educacio, Cultura i Universitats del Govern de les Illes Balears
- European Social Fund
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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 the input layer containing the information to be processed. One key advantage of this approach is that it can be implemented efficiently in hardware. We show that the reservoir computing implementation, in this case optoelectronic, is also capable to realize extreme learning machines, demonstrating the unified framework for both schemes in software as well as in hardware.
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