Recurrent neural networks (RNN) are being extensively exploited in industry to address complex predictive tasks by leveraging on the increased availability of data from processes. However, the rationale behind model response is encoded in an implicit way, which is difficult to be explained by practitioners. If revealed, such mechanisms could provide deeper insights into RNN execution, enhancing conventional performance evaluations. We propose a new approach based on the introduction of a model-based clustering layer, constraining the network to operate on a discrete latent state representation. By processing context-input conditioned transitions between clusters, a Moore Machine characterizing the RNN computations is extracted. The proposed approach is demonstrated on both synthetic experiments from an open benchmark problem and via the application to a pilot industrial plant, by the behavior cloning of the flexible conveyor of a Remanufacturing process. The finite-state RNN attains the prediction accuracy of RNN with continuous state, providing in addition a more interpretable structure.

循环神经网络（RNN）在工业中被广泛利用，以利用过程数据的更多可用性来解决复杂的预测任务。但是，模型响应背后的基本原理是以隐式方式编码的，因此从业人员很难解释。如果发现这些机制，则可以为RNN执行提供更深入的见解，从而增强常规的性能评估。我们在引入基于模型的群集层的基础上提出了一种新方法，该方法限制了网络在离散的潜在状态表示上进行操作。通过处理集群之间的上下文输入条件转换，提取了表征RNN计算的摩尔机。通过再制造过程的柔性传送带的行为克隆，从开放基准问题开始的综合实验以及通过将其应用于中试工业工厂，都证明了所提出的方法。有限状态RNN在连续状态下达到RNN的预测精度，此外还提供了更易解释的结构。