Estimation for Compositional Data using Measurements from Nonlinear Systems using Artificial Neural Networks
This work addresses the challenge of system inversion in nonlinear contexts, which is incremental as it extends existing linear methods to more complex scenarios.
The paper tackles the problem of estimating unknown compositional inputs from output responses of unknown nonlinear systems by using artificial neural networks to approximate the system inverse, achieving results competitive with optimal bounds for linear systems.
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.