Abstract
In this paper we present a unified framework for extreme learning machines and reservoir computing (echo state networks), which can be physically implemented using a single nonlinear neuron subject to delayed feedback. The reservoir is built within the delay-line, employing a number of “virtual” neurons. These virtual neurons receive random projections from the input layer containing the information to be processed. One key advantage of this approach is that it can be implemented efficiently in hardware. We show that the reservoir computing implementation, in this case optoelectronic, is also capable to realize extreme learning machines, demonstrating the unified framework for both schemes in software as well as in hardware.
Highlights
In this paper we propose a unified framework for random-projection machines, based on ESNs and ELMs
Since noise is an intrinsic component of the hardware system, we provide a sensitivity analysis to test the influence of the noise-induced performance degradation in the optoelectronic setup
We find that the signal to noise ratio (SNR) of a single measurement is ~24 dB, the experimental SNR can be increased to 40 dB by averaging the detection over ten repetitions of the measurement
Summary
In this paper we propose a unified framework for random-projection machines, based on ESNs and ELMs. Both ELMs and ESNs belong to the category of supervised learning machines, aimed at learning from data the conditional expected value of a m-dimensional output y(n) as a function of a d-dimensional input x(n), based on a number of examples n = 1, ..., N. The particularity of these approaches is the use of an intermediate reservoir space, where inputs are randomly projected in a nonlinear form, yielding a new, transformed D-dimensional r(n) space (typically D d).
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