The application of neural networks to non-linear dynamic system identification tasks has a long history, which consists mostly of autoregressive approaches. Autoregression, the usage of the model outputs of previous time steps, is a method of transferring a system state between time steps, which is not necessary for modeling dynamic systems with modern neural network structures, such as gated recurrent units (GRUs) and Temporal Convolutional Networks (TCNs). We compare the accuracy and execution performance of autoregressive and non-autoregressive implementations of a GRU and TCN on the simulation task of three publicly available system identification benchmarks. Our results show, that the non-autoregressive neural networks are significantly faster and at least as accurate as their autoregressive counterparts. Comparisons with other state-of-the-art black-box system identification methods show, that our implementation of the non-autoregressive GRU is the best performing neural network-based system identification method, and in the benchmarks without extrapolation, the best performing black-box method.

中文翻译：

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非自回归与自回归神经网络的系统识别

神经网络在非线性动态系统识别任务中的应用已有很长的历史，主要由自回归方法组成。自回归是先前时间步长的模型输出的一种用法，是一种在时间步长之间转移系统状态的方法，这对于使用现代神经网络结构（例如门控递归单元（GRU）和时间卷积）建模动态系统而言不是必需的网络（TCN）。在三个公开可用的系统识别基准的模拟任务上，我们比较了GRU和TCN的自回归和非自回归实现的准确性和执行性能。我们的结果表明，非自回归神经网络明显快于自回归神经网络，并且至少与它们的自回归神经网络一样准确。