Neural network models become increasingly popular as dynamic modeling tools in the control community. They have many appealing features including nonlinear structures, being able to approximate any functions. While most researchers hold optimistic attitudes towards such models, this paper questions the capability of (deep) neural networks for the modeling of dynamic systems using input-output data. For the identification of linear time-invariant (LTI) dynamic systems, two representative neural network models, Long Short-Term Memory (LSTM) and Cascade Foward Neural Network (CFNN) are compared to the standard Prediction Error Method (PEM) of system identification. In the comparison, four essential aspects of system identification are considered, then several possible defects and neglected issues of neural network based modeling are pointed out. Detailed simulation studies are performed to verify these defects: for the LTI system, both LSTM and CFNN fail to deliver consistent models even in noise-free cases; and they give worse results than PEM in noisy cases.

翻译：神经网络模型作为控制领域中的动态建模工具日益流行。它们具有诸多吸引人的特性，包括非线性结构以及逼近任意函数的能力。尽管大多数研究者对此类模型持乐观态度，本文却质疑了（深度）神经网络在利用输入-输出数据对动态系统进行建模方面的能力。针对线性时不变（LTI）动态系统的辨识问题，本文将两种代表性的神经网络模型——长短期记忆网络（LSTM）和级联前馈神经网络（CFNN）——与系统辨识中的标准预测误差方法（PEM）进行了比较。在比较中，考虑了系统辨识的四个基本方面，并指出了基于神经网络的建模中可能存在的若干缺陷和被忽视的问题。通过详细的仿真研究验证了这些缺陷：对于LTI系统，即使在无噪声的情况下，LSTM和CFNN均无法提供一致的模型；而在含噪声的情况下，它们的结果比PEM更差。