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
Current publications
-
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),
S. 1950053.
Verfügbar unter: 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, Hrsg.,
Applied data science : lessons learned for the data-driven business.
Cham:
Springer.
S. 371-386.
Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_20
-
Schwendner, Peter; Schüle, Martin; Hillebrand, Martin,
2019.
Sentiment analysis of European bonds 2016 - 2018.
Frontiers in Artificial Intelligence.
2(20).
Verfügbar unter: https://doi.org/10.21256/zhaw-18487
-
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.
-
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
-
Entwicklungsprototypen für die nachhaltige Digitalisierung der Lehre
Die COVID-19 Pandemie hat die Digitalisierung der Hochschullehre vorwärts katapultiert. Dadurch entstehen wertvolle Informationen, die für die nachhaltige Digitalisierung der Lehre relevant sind. Das Projekt erhebt strukturiert evidenzbasiert die Lehrerfahrungen und digitalen Kompetenzen an den Departementen Life Science and Facility Management und ...
-
PiaBreed: Machine Learning zur automatisierten Ovulations- und Geburtsüberwachung am Pferd
In diesem Projekt geht es um die Erarbeitung einer Datengrundlage (Piavita/ Uni Bern) und die Entwicklung eines mobilen, nichtinvasiven Systems (Piavita/ZHAW) für Tierärzte und Züchter. Ziele sind, die Erhebung wichtiger Vitaldaten und die Entwicklung von Algorithmen, um die Ovulation bei Stuten ohne Rektalkontrolle zuverlässig festzustellen sowie ...