  New global statistical models of nuclidic (atomic) masses based on
multilayered feedforward networks are developed. One goal of such studies is to
determine how well the existing data, and only the data, determines the mapping
from the proton and neutron numbers to the mass of the nuclear ground state.
Another is to provide reliable predictive models that can be used to forecast
mass values away from the valley of stability. Our study focuses mainly on the
former goal and achieves substantial improvement over previous neural-network
models of the mass table by using improved schemes for coding and training. The
results suggest that with further development this approach may provide a
valuable complement to conventional global models.
