Bio-Inspired Modeling & Learning Systems
We are concerned with design and development of adaptive systems for industrial and business applications, based on our expertise on machine learning, (recurrent) neural networks and bio-inspired algorithms as well as different simulation methods. A central speciality of our group is the development and research on complex (i.e. multi-methodical) forecasting systems. The polysemy of the term «LEARNING SYSTEMS» is an inherent element of the group’s spirit. Our self-concept as a team is the concept of a learning system. We are keen to learn more about new methods and more about ourselves in research and teaching. We are involved in teaching at Bachelor’s and Master’s level as well as continuing education and we actively work on the development of novel teaching methods to learn for our students and together with our students.
"Intelligence is based on learning"
- Bio-inspired algorithms and neural networks
- Human in the loop machine learning
- Modeling of complex systems
- Self-learning systems for real world applications
- Forecasting methodologies
Christen, Markus; Ott, Thomas; Schwarz, Daniel,
Advances in Complex Systems.
16(6), pp. 1350011.
Available from: https://doi.org/10.1142/S0219525913500112
NDES 2013, 21st Nonlinear Dynamics of Electronic Systems Conference, Bari, Italy, 2013.
Böck, Ronald; Glüge, Stefan; Siegert, Ingo; Wendemuth, Andreas,
Proceedings : 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction - ACII 2013.
ACII 2013 Humaine Association Conference, Geneva, 2-5 September 2013.
IEEE Institute of Electrical and Electronics Engineers.
Available from: https://doi.org/10.1109/ACII.2013.150
Uwate, Yoko; Ott, Thomas; Nishio, Yoshifumi,
European Conference on Circuit Theory and Design (ECCTD), Dresden, Deutschland, 8-12 September 2013.
Available from: https://doi.org/10.1109/ECCTD.2013.6662220
Takamaru, Yuji; Uwate, Yoko; Ott, Thomas; Nishio, Yoshifumi,
Journal of Signal Processing.
17(4), pp. 103-106.
Available from: https://doi.org/10.2299/jsp.17.103
Employing Natural Language Processing to identify inconsistencies in companies’ non-financial communication
NLP &ML tools for Swiss asset managers and owners for identifying inconsistencies in companies’ financial and non-financial communication
Bestimmung von Reduktionsfaktoren für Photovoltaik-Dachpotenzial
Sonnendach.ch wird aktuell vom BFE bezüglich der solaren Einstrahlung und die Dachflächen überarbeitet und erneuert. Gleichzeitig soll mit dem vorliegenden Projekt das auf Schweizer Dächern vorhandene Potenzial für PV-Strom aktualisiert und die Genauigkeit verbessert werden. Bedingt durch die Grösse von ...