27 citations · 30 across the 9 of their papers we have counts for
8 papers · 1 filter
Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangian
Lakshmi Priya P. K., Andreas Schwung
The paper proposes a novel data-driven framework for designing and training a feedback linearizing controller by explicitly incorporating relative degree based conditions into the…
Layer-Specific Lipschitz Modulation for Fault-Tolerant Multimodal Representation Learning
Diyar Altinses, Andreas Schwung
Modern multimodal systems deployed in industrial and safety-critical environments must remain reliable under partial sensor failures, signal degradation, or cross-modal inconsisten…
Prior-Informed Neural Network Initialization: A Spectral Approach for Function Parameterizing Architectures
David Orlando Salazar Torres, Diyar Altinses, Andreas Schwung
Neural network architectures designed for function parameterization, such as the Bag-of-Functions (BoF) framework, bridge the gap between the expressivity of deep learning and the…
Data driven feedback linearization of nonlinear control systems via Lie derivatives and stacked regression approach
Lakshmi Priya P. K., Andreas Schwung
Discovering the governing equations of a physical system and designing an effective feedback controller remains one of the most challenging and intensive areas of ongoing research.…
Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach
Sasa Ilic, Abdulkerim Karaman, Johannes Pöppelbaum +3
This study presents a novel approach for predicting wall thickness changes in tubes during the nosing process. Specifically, we first provide a thorough analysis of nosing processe…
Improving Quaternion Neural Networks with Quaternionic Activation Functions
Johannes Pöppelbaum, Andreas Schwung
In this paper, we propose novel quaternion activation functions where we modify either the quaternion magnitude or the phase, as an alternative to the commonly used split activatio…
Time Series Compression using Quaternion Valued Neural Networks and Quaternion Backpropagation
Johannes Pöppelbaum, Andreas Schwung
We propose a novel quaternionic time-series compression methodology where we divide a long time-series into segments of data, extract the min, max, mean and standard deviation of t…
Quaternion Backpropagation
Johannes Pöppelbaum, Andreas Schwung
Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for opti…