Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalization

• Published in: IEEE transaction on neural networks and learning systems Vol. 28; no. 11; pp. 2686 - 2698
• Main Authors: Antonik, Piotr, Duport, Francois, Hermans, Michiel, Smerieri, Anteo, Haelterman, Marc, Massar, Serge
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial Intelligence; Artificial neural networks; channel equalization; Communications systems; Computer Science; Computer Vision and Pattern Recognition; Computers; Computing time; Distance learning; Electronics; Engineering Sciences; Equalization; Field programmable gate arrays; field-programmable gate array (FPGA); Machine Learning; Nonlinear distortion; Online instruction; online learning; Optics; optoelectronic systems; Optoelectronics; Photonic; Real-time systems; reservoir computing (RC); Reservoirs; Speech recognition; Switching; Training; Wireless communication; Wireless communications; opto-electronic systems; reservoir computing; channel equalisation; FPGA; Artificial neural networks; online learning

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 th...