论文标题
使用人工神经网络的非线性系统测量的组成数据估算
Estimation for Compositional Data using Measurements from Nonlinear Systems using Artificial Neural Networks
论文作者
论文摘要
我们的目标是通过训练集估算原始系统的倒数后，通过未知系统估算其输出响应的未知组成输入。使用人工神经网络（ANN）的提出方法可以与凸优化理论应用的线性系统的最佳界限竞争，并为非线性系统反演展示了有希望的结果。我们通过设计许多不同类型的非线性系统进行了广泛的实验。
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.
