论文标题
人工神经网络鲁棒性的混合目标函数 - 机械系统中参数的估计
A Hybrid Objective Function for Robustness of Artificial Neural Networks -- Estimation of Parameters in a Mechanical System
论文作者
论文摘要
在几项研究中，与普通数据驱动的神经网络相比，与嘈杂的输入数据相比，混合神经网络已被证明更强大。我们考虑基于加速度曲线的机械车辆模型的参数的任务。我们介绍了卷积神经网络结构，该结构能够预测未知参数不同的车辆模型家族的参数。我们介绍了一个卷积神经网络体系结构，该卷积神经网络体系结构给出了顺序数据可预测基础数据动力学的参数。该网络通过两个目标功能进行培训。第一个构成了一种更为幼稚的方法，假定真实参数是已知的。第二个目标结合了基本动力学的知识，因此被视为混合方法。我们表明，就鲁棒性而言，后者的表现优于嘈杂输入数据的第一个目标。
In several studies, hybrid neural networks have proven to be more robust against noisy input data compared to plain data driven neural networks. We consider the task of estimating parameters of a mechanical vehicle model based on acceleration profiles. We introduce a convolutional neural network architecture that is capable to predict the parameters for a family of vehicle models that differ in the unknown parameters. We introduce a convolutional neural network architecture that given sequential data predicts the parameters of the underlying data's dynamics. This network is trained with two objective functions. The first one constitutes a more naive approach that assumes that the true parameters are known. The second objective incorporates the knowledge of the underlying dynamics and is therefore considered as hybrid approach. We show that in terms of robustness, the latter outperforms the first objective on noisy input data.
