Abstract
Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals. Its analog implementations equal and sometimes outperform other digital algorithms on a series of benchmark tasks. Their performance can be increased by switching from offline to online training method. Here we present the first online trained opto-electronic reservoir computer. The system is tested on a channel equalisation task and the algorithm is executed by an FPGA chip. We report performances close to previous implementations and demonstrate the benefits of online training on a non-stationary task that could not be easily solved using offline methods.
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Acknowledgements
We acknowledge financial support by Interuniversity Attraction Poles program of the Belgian Science Policy Office, under grant IAP P7-35 photonics@be and by the Fonds de la Recherche Scientifique FRS-FNRS.
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Antonik, P., Duport, F., Smerieri, A., Hermans, M., Haelterman, M., Massar, S. (2015). Online Training of an Opto-Electronic Reservoir Computer. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_27
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