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"
Expertise
- 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
Topics
Our Team
Prof. Dr. Thomas Ott, Deputy Head of the Institute of Applied Simulation
Some Projects in a Nutshell
Current publications
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Uwate, Yoko; Takamaru, Yuji; Ott, Thomas; Nishio, Yoshifumi,
2019.
Clustering using chaotic circuit networks with weighted couplings.
International Journal of Bifurcation and Chaos.
29(4),
pp. 1950053.
Available from: https://doi.org/10.1142/S0218127419500536
-
Ott, Thomas; Glüge, Stefan; Bödi, Richard; Kauf, Peter,
2019.
Economic measures of forecast accuracy for demand planning : a case-based discussion
.
In:
Braschler, M.; Stadelmann, T.; Stockinger, K., eds.,
Applied data science : lessons learned for the data-driven business.
Cham:
Springer.
pp. 371-386.
Available from: https://doi.org/10.1007/978-3-030-11821-1_20
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Schwendner, Peter; Schüle, Martin; Hillebrand, Martin,
2019.
Sentiment analysis of European bonds 2016–2018.
Frontiers in Artificial Intelligence.
2(20).
Available from: https://doi.org/10.21256/zhaw-18487
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Ott, Thomas; Glüge, Stefan; Schüle, Martin; Hill, Christopher,
2018.
A dynamic network approach for the analysis of pathogen transmission chains.
In:
26th International Conference on Nonlinear Dynamics in Electronic Systems, Acireale, Italy, Juni 2018.
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Schüle, Martin; Ott, Thomas; Schwendner, Peter,
2018.
Influence networks in financial markets : forecast scenarios.
In:
NDES 2018, The 26th Nonlinear Dynamics of Electronic Systems Conference, Acireale, June, 11-13 2018.
Current projects
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Fighting bites with bytes: Promoting public health with crowdsourced tick prevention
Ticks are on the rise and transmit several infectious diseases, leading to serious illness or even death. The smartphone App “Zecke–Tick Prevention helps people, to remember the tick bite location and to check it for potential Lyme disease symptoms. In an interdisciplinary approach, ZHAW-scientists want to find out more about spatial tick risks. ...
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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 ...