Estimation for Compositional Data using Measurements from Nonlinear Systems using Artificial Neural Networks
Machine Learning
2020-01-27 v1 Neural and Evolutionary Computing
Optimization and Control
Statistics Theory
Machine Learning
Statistics Theory
Abstract
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.
Cite
@article{arxiv.2001.09040,
title = {Estimation for Compositional Data using Measurements from Nonlinear Systems using Artificial Neural Networks},
author = {Se Un Park},
journal= {arXiv preprint arXiv:2001.09040},
year = {2020}
}
Comments
43 pages, 20 figures