  Our objective is to estimate the unknown compositional input from its output
response through an unknown system after estimating the inverse of the original
system with a training set. The proposed methods using artificial neural
networks (ANNs) can compete with the optimal bounds for linear systems, where
convex optimization theory applies, and demonstrate promising results for
nonlinear system inversions. We performed extensive experiments by designing
numerous different types of nonlinear systems.
