Journal ArticleDOI
Minimum Complexity Echo State Network
Ali Rodan,Peter Tino +1 more
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TLDR
It is shown that a simple deterministically constructed cycle reservoir is comparable to the standard echo state network methodology and the (short-term) of linear cyclic reservoirs can be made arbitrarily close to the proved optimal value.Abstract:
Reservoir computing (RC) refers to a new class of state-space models with a fixed state transition structure (the reservoir) and an adaptable readout form the state space. The reservoir is supposed to be sufficiently complex so as to capture a large number of features of the input stream that can be exploited by the reservoir-to-output readout mapping. The field of RC has been growing rapidly with many successful applications. However, RC has been criticized for not being principled enough. Reservoir construction is largely driven by a series of randomized model-building stages, with both researchers and practitioners having to rely on a series of trials and errors. To initialize a systematic study of the field, we concentrate on one of the most popular classes of RC methods, namely echo state network, and ask: What is the minimal complexity of reservoir construction for obtaining competitive models and what is the memory capacity (MC) of such simplified reservoirs? On a number of widely used time series benchmarks of different origin and characteristics, as well as by conducting a theoretical analysis we show that a simple deterministically constructed cycle reservoir is comparable to the standard echo state network methodology. The (short-term) of linear cyclic reservoirs can be made arbitrarily close to the proved optimal value.read more
Citations
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Journal ArticleDOI
Information processing using a single dynamical node as complex system
Lennert Appeltant,Miguel C. Soriano,G. Van der Sande,Jan Danckaert,Serge Massar,Joni Dambre,Benjamin Schrauwen,Claudio R. Mirasso,Ingo Fischer +8 more
TL;DR: This work introduces a novel architecture that reduces the usually required large number of elements to a single nonlinear node with delayed feedback and proves that delay-dynamical systems, even in their simplest manifestation, can perform efficient information processing.
Journal ArticleDOI
Parallel photonic information processing at gigabyte per second data rates using transient states
TL;DR: The potential of a simple photonic architecture to process information at unprecedented data rates is demonstrated, implementing a learning-based approach and all digits with very low classification errors are identified and chaotic time-series prediction with 10% error is performed.
Journal ArticleDOI
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
TL;DR: This paper constitutes the first holistic tutorial on the development of ANN-based ML techniques tailored to the needs of future wireless networks and overviews how artificial neural networks (ANNs)-based ML algorithms can be employed for solving various wireless networking problems.
Journal ArticleDOI
Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing.
Laurent Larger,Miguel C. Soriano,Daniel Brunner,Lennert Appeltant,José M. Gutiérrez,Luis Pesquera,Claudio R. Mirasso,Ingo Fischer +7 more
TL;DR: This work experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator subject to delayed feedback and implements a neuro-inspired concept, called Reservoir Computing, proven to possess universal computational capabilities.
Book ChapterDOI
A Practical Guide to Applying Echo State Networks
TL;DR: Practical techniques and recommendations for successfully applying Echo State Network, as well as some more advanced application-specific modifications are presented.
References
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Journal ArticleDOI
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Herbert Jaeger,Harald Haas +1 more
TL;DR: A method for learning nonlinear systems, echo state networks (ESNs), which employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains is presented.
Journal ArticleDOI
A Two-dimensional Mapping with a Strange Attractor
TL;DR: In this article, the same properties can be observed in a simple mapping of the plane defined by: \({x i + 1}} = {y_i} + 1 - ax_i^2,{y i+ 1} = b{x_i}\).
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