dc.description.abstract | Reservoir computing is an approach to machine learning. Typical reservoir computing
approaches use large, untrained artificial neural networks to transform an input signal. To
produce the desired output, a readout layer is trained using linear regression on the neural
network.
Recently, several attempts have been made using other kinds of dynamic systems
instead of artificial neural networks. Cellular automata are an example of a dynamic sys-
tem that has been proposed as a replacement.-
This thesis attempts to discover whether cellular automata are a viable candidate for
use in reservoir computing. Four different tasks solved by other reservoir computing sys-
tems are attempted with elementary cellular automata, a limited subset of all possible
cellular automata. The effect of changing different properties of the cellular automata
are investigated, and the results are compared with the results when performing the same
experiments with typical reservoir computing systems.
Reservoir computing seems like a potentially very interesting utilization of cellular
automata. However, it is evident that more research into this field is necessary to reach
performance comparable to existing reservoir computing systems. | |