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
Reference projects
Comprehensive Sales Forecasting for Supply Chain Optimization (CTI project)
Germ Tracking - Project Infectiology ++
Fighting bites with bytes: Promoting public health with crowdsourced tick prevention
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, Martin; Stadelmann, Thilo; Stockinger, Kurt, 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:
The 26th Nonlinear Dynamics of Electronic Systems Conference, (NDES 2018), Acireale, 11-13 June 2018.
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Schüle, Martin; Ott, Thomas; Schwendner, Peter,
2018.
Influence networks in financial markets : forecast scenarios.
In:
NDES 2018, 26th Nonlinear Dynamics of Electronic Systems Conference, Acireale, Italy, June, 11-13 2018.
Current projects
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Prototypes for the Sustainable Digitisation of University Teaching
The COVID-19 Pandemy has forced higher education into fast forwarding their digitisation across the board. This creates valuable and relevant information for the sustainable digitization beyond the crisis mode. The project structures evidence-based digital teaching exsperiences and digital competences at the departments Life Science and Facility ...
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PiaBreed: Machine Learning for automated ovulation and birth monitoring in horses
The project comprises the tasks of a comprehensive data collection (Piavita/ University of Bern) and the development of a mobile, non-invasive system (Piavita/ZHAW) for veterinarians and breeders. The goal is to collect important vital data and to develop a new algorithm scheme with which - ovulation in mares can be reliably determined without ...