Adopting attention-mechanisms for Neural Logic Rule Layers
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Jan Niclas Reimann
Jan Niclas Reimann received his B. Eng. in electrical engineering and his M. Sc. in Systems Engineering from the University of applied Sciences Soest, Germany. He has been working as a research assistant and Ph. D. student since late 2018 at the department for automation technology and learning systems at the University of applied Sciences Soest, Germany. His main research falls into the field of interpretability and explainability analysis with regards to deep learning, especially supervised learning.
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Andreas Schwung
Andreas Schwung received the Ph. D. degree in electrical engineering from the Technische Universität Darmstadt, Darmstadt, Germany, in 2011. From 2011 to 2015, he was an R&D Engineer with MAN Diesel & Turbo SE, Oberhausen, Germany. Since 2015, he has been a Professor of automation technology at the South Westphalia University of Applied Sciences, Soest, Germany. His research interests include model based control, networked automation systems, and intelligent data analytics with applications in manufacturing and process industry.
Steven X. Ding received Ph. D. degree in electrical engineering from the Gerhard-Mercator University of Duisburg, Germany, in 1992. From 1992 to 1994, he was a R&D engineer at Rheinmetall GmbH. From 1995 to 2001, he was a professor of control engineering at the University of Applied Science Lausitz in Senftenberg, Germany, and served as vice president of this university during 1998–2000. Since 2001, he has been a chair professor of control engineering and the head of the Institute for Automatic Control and Complex Systems (AKS) at the University of Duisburg-Essen, Germany. His research interests are model-based and data-driven fault diagnosis, control and fault-tolerant systems as well as their applications in industry with a focus on automotive systems, chemical processes and renewable energy systems.
Abstract
In previous works we discovered that rule-based systems severely suffer in performance when increasing the number of rules. In order to increase the amount of possible boolean relations while keeping the number of rules fixed, we employ ideas from well known Spatial Transformer Systems and Self-Attention Networks: here, our learned rules are not static but are dynamically adjusted to fit the input data by training a separate rule-prediction system, which is predicting parameter matrices used in Neural Logic Rule Layers. We show, that these networks, termed Adaptive Neural Logic Rule Layers, outperform their static counterpart both in terms of final performance, as well as training stability and excitability during early stages of training.
Zusammenfassung
In früheren Arbeiten haben wir dargelegt, dass neurale regelbasierte Systeme deutliche Probleme bekommen, wenn eine große Anzahl an Regeln gelernt werden muss. Um mehr Kombinationen zuzulassen, ohne die Anzahl der Regeln zu erhöhen, wenden wir grundlegende Ideen anderer Arbeiten wie Spatial Transformer oder Self-Attention Netze auf eigens entwickelte Netze an. Unsere Regeln sind so nicht mehr statisch, sondern veränderlich und passen sich dynamisch an den Eingang des Netzes an. Realisiert wird dies durch weitere, parallel geschaltete, Netze, um unsere ansonsten gelernten Gewichtsmatrizen vorherzusagen anstatt diese direkt zu trainieren. Im Vergleich zum statischen Ansatz erreichen wir nicht nur gleichwertige Klassifizierungsergebnisse, sondern erhöhen deutlich die Stabilität und Erregbarkeit während des frühen Trainings.
Dedicated to the 60th birthday of Prof. Dr.-Ing. Jürgen Adamy.
About the authors
Jan Niclas Reimann received his B. Eng. in electrical engineering and his M. Sc. in Systems Engineering from the University of applied Sciences Soest, Germany. He has been working as a research assistant and Ph. D. student since late 2018 at the department for automation technology and learning systems at the University of applied Sciences Soest, Germany. His main research falls into the field of interpretability and explainability analysis with regards to deep learning, especially supervised learning.
Andreas Schwung received the Ph. D. degree in electrical engineering from the Technische Universität Darmstadt, Darmstadt, Germany, in 2011. From 2011 to 2015, he was an R&D Engineer with MAN Diesel & Turbo SE, Oberhausen, Germany. Since 2015, he has been a Professor of automation technology at the South Westphalia University of Applied Sciences, Soest, Germany. His research interests include model based control, networked automation systems, and intelligent data analytics with applications in manufacturing and process industry.
Steven X. Ding received Ph. D. degree in electrical engineering from the Gerhard-Mercator University of Duisburg, Germany, in 1992. From 1992 to 1994, he was a R&D engineer at Rheinmetall GmbH. From 1995 to 2001, he was a professor of control engineering at the University of Applied Science Lausitz in Senftenberg, Germany, and served as vice president of this university during 1998–2000. Since 2001, he has been a chair professor of control engineering and the head of the Institute for Automatic Control and Complex Systems (AKS) at the University of Duisburg-Essen, Germany. His research interests are model-based and data-driven fault diagnosis, control and fault-tolerant systems as well as their applications in industry with a focus on automotive systems, chemical processes and renewable energy systems.
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© 2022 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Survey
- Data fusion for automated driving: An introduction
- Methoden
- Distributed optimization methods for N-cluster games
- On linear-quadratic optimal transition in synchronization of homogeneous multi-agent systems
- Adopting attention-mechanisms for Neural Logic Rule Layers
- Dynamische visuelle Passermarken
- Systematic mitigation of gain scheduling induced windup phenomena
- Anwendungen
- Fahrplanoptimierung von Windenergieanlagen ohne feste Einspeisevergütung
- On reference trajectory generation for overhead crane travel movements
- Persönliches
- Prof. Dr.-Ing. Jürgen Adamy zum 60. Geburtstag
Artikel in diesem Heft
- Frontmatter
- Survey
- Data fusion for automated driving: An introduction
- Methoden
- Distributed optimization methods for N-cluster games
- On linear-quadratic optimal transition in synchronization of homogeneous multi-agent systems
- Adopting attention-mechanisms for Neural Logic Rule Layers
- Dynamische visuelle Passermarken
- Systematic mitigation of gain scheduling induced windup phenomena
- Anwendungen
- Fahrplanoptimierung von Windenergieanlagen ohne feste Einspeisevergütung
- On reference trajectory generation for overhead crane travel movements
- Persönliches
- Prof. Dr.-Ing. Jürgen Adamy zum 60. Geburtstag
