论文标题
深度能量的NARX模型
Deep Energy-Based NARX Models
论文作者
论文摘要
本文针对基于系统输入数据学习非线性ARX模型的问题。特别是，我们的兴趣是学习基于过去输入和输出的有限窗口的有条件分布。为了实现这一目标，我们考虑使用所谓的基于能量的模型，这些模型是在盟军领域开发的，用于根据数据学习未知分布。基于能量的模型依靠一般函数来描述分布，在这里，我们考虑了一个深层神经网络。这种方法的主要好处是它能够学习简单且高度复杂的噪声模型，我们在模拟和实验数据上证明了这一点。
This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data. In particular, our interest is in learning a conditional distribution of the current output based on a finite window of past inputs and outputs. To achieve this, we consider the use of so-called energy-based models, which have been developed in allied fields for learning unknown distributions based on data. This energy-based model relies on a general function to describe the distribution, and here we consider a deep neural network for this purpose. The primary benefit of this approach is that it is capable of learning both simple and highly complex noise models, which we demonstrate on simulated and experimental data.
