Estimation for Compositional Data using Measurements from Nonlinear Systems using Artificial Neural Networks

• URL: http://arxiv.org/abs/2001.09040v1
• Date: Fri, 24 Jan 2020 14:50:13 GMT
• Title: Estimation for Compositional Data using Measurements from Nonlinear Systems using Artificial Neural Networks
• Authors: Se Un Park
• Abstract summary: The proposed methods using artificial neural networks (ANNs) can compete with the optimal bounds for linear systems. We performed extensive experiments by designing numerous different types of nonlinear systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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