par Antonik, Piotr 
;Duport, Francois ;Hermans, Michiel ;Smerieri, Anteo ;Haelterman, Marc ;Massar, Serge
Référence IEEE Transactions on Neural Networks and Learning Systems, PP, 99, 7553521
Publication Publié, 2016-08
;Duport, Francois ;Hermans, Michiel ;Smerieri, Anteo ;Haelterman, Marc ;Massar, Serge Référence IEEE Transactions on Neural Networks and Learning Systems, PP, 99, 7553521
Publication Publié, 2016-08
Article révisé par les pairs
| Résumé : | Reservoir computing is a bioinspired computing paradigm for processing time-dependent signals. The performance of its analog implementation is comparable to other state-of-the-art algorithms for tasks such as speech recognition or chaotic time series prediction, but these are often constrained by the offline training methods commonly employed. Here, we investigated the online learning approach by training an optoelectronic reservoir computer using a simple gradient descent algorithm, programmed on a field-programmable gate array chip. Our system was applied to wireless communications, a quickly growing domain with an increasing demand for fast analog devices to equalize the nonlinear distorted channels. We report error rates up to two orders of magnitude lower than previous implementations on this task. We show that our system is particularly well suited for realistic channel equalization by testing it on a drifting and a switching channel and obtaining good performances. |
