Recent work has shown that computational substrates
made from carbon nanotube/polymer mixtures can form
trainable Reservoir Computers. This new reservoir computing
platform uses computer based evolutionary algorithms to optimise
a set of electrical control signals to induce reservoir
properties within the substrate. In the training process, evolution
decides the value of analogue control signals (voltages) and the
location of inputs and outputs on the substrate that improve the
performance of the subsequently trained reservoir readout.
Here, we evaluate the performance of evolutionary search
compared to randomly assigned electrical configurations. The
substrate is trained and evaluated on time-series prediction using
the Santa Fe Laser generated competition data (dataset A). In
addition to the main investigation, we introduce two new features
closely linked to the traditional reservoir computing architecture,
adding an evolvable input-weighting mechanism and a reservoir
time-scaling parameter.
The experimental results show evolved configurations across
all four test substrates consistently produce reservoirs with
greater performance than randomly configured reservoirs. The
results also show that applying both input-weighting and timescaling
simultaneously can provide additional tuning to the task,
improving performance. For one material, the evolved reservoir
is shown to outperform – for this task – all other hardwarebased
reservoir computers found in the literature. The same
material also outperforms a simple evolved simulated Echo State
Network of the same size. The performance of this material is
reported to be both consistent after long time-periods and after
reconfiguration to other tasks.
full paper: PDF | doi:10.1109/SSCI.2016.7850170
@inproceedings(Dale-ICES-2016, author = "Matthew Dale and Julian F. Miller and Susan Stepney and Martin A. Trefzer", title = "Reservoir Computing {\it in Materio}: An Evaluation of Configuration through Evolution", doi = "10.1109/SSCI.2016.7850170", crossref = "ICES-2016" ) @proceedings(ICES-2016, title = "ICES 2016 at SSCI 2016, Athens, Greece, December 2016", booktitle = "ICES 2016 at SSCI 2016, Athens, Greece, December 2016", publisher = "IEEE", year = 2016 )