Delay-based Reservoir Computing: Noise Effects in a Combined Analog and Digital Implementation
Soriano, Miguel C.; Ortín, Silvia; Keuninckx, Lars; Appeltant, Lennert; Danckaert, Jan; Pesquera, Luis; Van der Sande, Guy
IEEE Transactions on Neural Networks and Learning Systems 26, 388-393 (2015)
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.