  The 21 cm signal from the Epoch of Reionization should be observed within the
next decade. While a simple statistical detection is expected with SKA
pathfinders, the SKA will hopefully produce a full 3D mapping of the signal. To
extract from the observed data constraints on the parameters describing the
underlying astrophysical processes, inversion methods must be developed. For
example, the Markov Chain Monte Carlo method has been successfully applied.
Here we test another possible inversion method: artificial neural networks
(ANN). We produce a training set which consists of 70 individual sample. Each
sample is made of the 21 cm power spectrum at different redshifts produced with
the 21cmFast code plus the value of three parameters used in the semi-numerical
simulations that describe astrophysical processes. Using this set we train the
network to minimize the error between the parameter values it produces as an
output and the true values. We explore the impact of the architecture of the
network on the quality of the training. Then we test the trained network on the
new set of 54 test samples with different values of the parameters. We find
that the quality of the parameter reconstruction depends on the sensitivity of
the power spectrum to the different parameters at a given redshift, that
including thermal noise and sample variance decreases the quality of the
reconstruction and that using the power spectrum at several redshifts as an
input to the ANN improves the quality of the reconstruction. We conclude that
ANNs are a viable inversion method whose main strength is that they require a
sparse exploration of the parameter space and thus should be usable with full
numerical simulations.
