1.	Alexander Rehmer, Marco Klute, Andreas Kroll and Hans-Peter Heim: An internal dynamics approach to predicting batch-end product quality in plastic injection molding using Recurrent Neural Networks, IFAC-PapersOnLine, 6th IEEE Conference on Control Technology and Applications (CCTA), 53, 1427-1432, Elsevier, 2022

   2.	Alexander Rehmer, Andreas Kroll: On affine quasi-LPV System Identification with unknown state-scheduling using (deep) Recurrent Neural Networks, IFAC-PapersOnLine, Proceedings of the 26th International Conference on System Theory Control and Computing (ICSTCC), 446-451, 2022

   3.	Alexander Rehmer, Andreas Kroll: Eine Python-Toolbox zur datengetriebenen Modellierung des Spritzgieprozsses und Lösung von Optimalsteuerungsproblemen zur Steuerung der Bauteilqualität, 32. Workshop Computational Intelligence, 133-150, KIT Scientific Publishing, https://library.oapen.org/handle/20.500.12657/59840?show=full, 2022

   4.	Alexander Rehmer, Andreas Kroll: A Deep Recurrent Neural Network model for affine quasi-LPV System identification, Preprints of the 20th European Control Conference (ECC), 566-571, 2022

   5.	Alexander Rehmer, Andras Kroll: The effect of the forget gate on bifurcation boundaries and dynamics in Recurrent Neural Networks and its implications for gradient-based optimization, Preprints of the International Joint Conference on Neural Networks (IJCNN 2022), 1-8, 2022

   6.	Alexander Rehmer, Andreas Kroll: On the vanishing and exploding gradient problem in Gated Recurrent Units, IFAC-PapersOnLine, 21th IFAC World Congress, 53, 2, 1243--1248, Elsevier, 2020

   7.	Alexander Rehmer, Andreas Kroll: On Using Gated Recurrent Units for Nonlinear System Identification, Preprints of the 18th European Control Conference (ECC), 2504-2509, https://www.ifac-control.org/events/european-control-conference-in-cooperation-with-ifac-ecc-2019, 2019