Journal
IFAC PAPERSONLINE
Volume 53, Issue 2, Pages 1243-1248Publisher
ELSEVIER
DOI: 10.1016/j.ifacol.2020.12.1342
Keywords
Nonlinear system identification; Recurrent Neural Networks; Gated Recurrent Units
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Recurrent Neural Networks are applied in areas such as speech recognition, natural language and video processing, and the identification of nonlinear state space models. Conventional Recurrent Neural Networks, e.g. the Elman Network, are hard to train. A more recently developed class of recurrent neural networks, so-called Gated Units, outperform their counterparts on virtually every task. This paper aims to provide additional insights into the differences between RNNs and Gated Units in order to explain the superior perfomance of gated recurrent units. It is argued, that Gated Units are easier to optimize not because they solve the vanishing gradient problem, but because they circumvent the emergence of large local gradients. Copyright (C) 2020 The Authors.
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