Encoder-decoder neural networks (EDNN) condense information most relevant to
the output of the feedforward network to activation values at a bottleneck
layer. We study the use of this architecture in emulation and interpretation of
simulated X-ray spectroscopic data with the aim to identify key structural
characteristics for the spectra, previously studied using emulator-based
component analysis (ECA). We find an EDNN to outperform ECA in covered target
variable variance, but also discover complications in interpreting the latent
variables in physical terms. As a compromise of the benefits of these two
approaches, we develop a network where the linear projection of ECA is used,
thus maintaining the beneficial characteristics of vector expansion from the
latent variables for their interpretation. These results underline the
necessity of information recovery after its condensation and identification of
decisive structural degrees for the output spectra for a justified
interpretation.

通过在模拟的 X 射线光谱数据中使用编码器 - 解码器神经网络 (EDNN)，我们研究了这种架构在模拟和解释光谱数据中的应用，以确定光谱的关键结构特征，发现 EDNN 在覆盖目标变量方差方面优于基于仿真器的成分分析 (ECA)，但在物理术语的潜变量解释方面存在一些困难，因此，我们开发了一个网络，使用 ECA 的线性投影，并保持了潜变量的矢量扩展的有益特性，同时强调了在将信息压缩后的信息恢复以及为合理解释确定关键结构度量的必要性。