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arXiv · 2011.06806

On the stability properties of Gated Recurrent Units neural networks

Fabio Bonassi·Marcello Farina·Riccardo Scattolini
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Abstract

The goal of this paper is to provide sufficient conditions for guaranteeing the Input-to-State Stability (ISS) and the Incremental Input-to-State Stability ({\delta}ISS) of Gated Recurrent Units (GRUs) neural networks. These conditions, devised for both single-layer and multi-layer architectures, consist of nonlinear inequalities on network's weights. They can be employed to check the stability of trained networks, or can be enforced as constraints during the training procedure of a GRU. The resulting training procedure is tested on a Quadruple Tank nonlinear benchmark system, showing satisfactory modeling performances.

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BibTeXRIS

Fabio Bonassi, Marcello Farina, Riccardo Scattolini. 2020-11-13. On the stability properties of Gated Recurrent Units neural networks. https://doi.org/10.1016/j.sysconle.2021.105049

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