Bio-Inspired Modeling & Learning Systems
Introduction - Research and Teaching
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.
Research Topics & Expertise
"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
Some Projects in a Nutshell
Uwate, Yoko; Takamaru, Yuji; Ott, Thomas; Nishio, Yoshifumi,
International journal of bifurcation and chaos.
Available from: https://doi.org/10.1142/S0218127419500536
Braschler, M.; Stadelmann, T.; Stockinger, K., eds.,
Applied Data Science.
Available from: https://doi.org/10.1007/978-3-030-11821-1_20
Glüge, Stefan; Böck, Ronald; Palm, Günther; Wendemuth, Andreas,
Available from: https://doi.org/10.1016/j.neucom.2013.11.043
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
An integrated modelling and learning framework for real-time online decision assistance in Swiss agriculture
We are developing an agricultural risk decision assistant based on a unique model that can assess and visualize reliable weather and seasonal climate forecasts, soil data, and crop growth forecasts. Based on real-time and historical weather, climate, soil and crop data and novel learning algorithms, the system calculates expected weather and ...
Smartstones - AI for plant breeding
Goal of the project is a feasibility study to evaluate the potential of diverse AI techniques for opimising plant breeding on the basis of morphological characteristics.