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

Autor:	Park, Se Un
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing Mathematics - Optimization and Control Mathematics - Statistics Theory Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 43 pages, 20 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.09040 Zobrazit plný text záznamu View this record from Arxiv