Title :
Delay-Based Reservoir Computing: Noise Effects in a Combined Analog and Digital Implementation
Author :
Soriano, M.C. ; Ortin, S. ; Keuninckx, L. ; Appeltant, L. ; Danckaert, J. ; Pesquera, L. ; van der Sande, G.
Author_Institution :
Inst. de Fis. Interdisciplinar y Sist. Complejos, IFISC, Palma de Mallorca, Spain
Abstract :
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.
Keywords :
analogue-digital conversion; chaos; digital-analogue conversion; learning (artificial intelligence); multiplexing; recurrent neural nets; signal classification; time series; ADC; DAC; analog-to-digital converter; chaotic time-series prediction task; classification problem; delay-based reservoir computing; digital-to-analog converter; machine learning; noise effect; nonlinear analog electronic circuit; recurrent neural networks; time-multiplexing; Delays; Hardware; Learning systems; Noise; Numerical simulation; Quantization (signal); Reservoirs; Delay systems; dynamical systems; electronic circuits; memory capacity; pattern recognition; reservoir computing (RC); time-series prediction; time-series prediction.;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2014.2311855