  Solar wind properties are determined by the conditions of their solar source
region and transport history. Solar wind parameters, such as proton speed,
proton density, proton temperature, magnetic field strength, and the charge
state composition of oxygen, are used as proxies to investigate the solar
source region of the solar wind. The transport and conditions in the solar
source region affect several solar wind parameters simultaneously. The observed
redundancy could be caused by a set of hidden variables. We test this
assumption by determining how well a function of four of the selected solar
wind parameters can model the fifth solar wind parameter. If such a function
provided a perfect model, then this solar wind parameter would be uniquely
determined from hidden variables of the other four parameters. We used a neural
network as a function approximator to model unknown relations between the
considered solar wind parameters. This approach is applied to solar wind data
from the Advanced Composition Explorer (ACE). The neural network
reconstructions are evaluated in comparison to observations. Within the limits
defined by the measurement uncertainties, the proton density and proton
temperature can be reconstructed well. We also found that the reconstruction is
most difficult for solar wind streams preceding and following stream
interfaces. For all considered solar wind parameters, but in particular the
proton density, temperature, and the oxygen charge-state ratio, parameter
reconstruction is hindered by measurement uncertainties. The reconstruction
accuracy of sector reversal plasma is noticeably lower than that of streamer
belt or coronal hole plasma. The fact that the oxygen charge-state ratio, a
non-transport-affected property, is difficult to reconstruct may imply that
recovering source-specific information from the transport-affected proton
plasma properties is challenging.
