论文标题
终生学习反复的神经网络以进行控制设计
Towards lifelong learning of Recurrent Neural Networks for control design
论文作者
论文摘要
本文提出了一种终身学习复发性神经网络的方法，例如NNARX，ESN，LSTM和GRU，被用作控制系统合成中的植物模型。该问题很重要，因为在许多实际应用中，需要在可用的新信息和/或系统进行更改时调整模型，而无需随着时间的推移而存储越来越多的数据。确实，在这种情况下，出现了许多问题，例如众所周知的灾难性遗忘和容量饱和。我们提出了一种受移动范围估计器启发的适应算法，从而得出了其收敛条件。所述方法应用于现有文献中已经具有挑战性的基准的模拟化学厂。讨论了获得的主要结果。
This paper proposes a method for lifelong learning of Recurrent Neural Networks, such as NNARX, ESN, LSTM, and GRU, to be used as plant models in control system synthesis. The problem is significant because in many practical applications it is required to adapt the model when new information is available and/or the system undergoes changes, without the need to store an increasing amount of data as time proceeds. Indeed, in this context, many problems arise, such as the well known Catastrophic Forgetting and Capacity Saturation ones. We propose an adaptation algorithm inspired by Moving Horizon Estimators, deriving conditions for its convergence. The described method is applied to a simulated chemical plant, already adopted as a challenging benchmark in the existing literature. The main results achieved are discussed.
