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
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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Berger, Verena; Müller, Claudia; Egeler, Gian-Andrea; Muir, Karen; Bradford, Sebastian; Delucchi, Matteo; Stucki, Matthias; et al.,
2021.
Energie- und klimabewusste Ernährung in städtischen Verpflegungsbetrieben.
Energieforschung Stadt Zürich.
Available from: https://doi.org/10.21256/zhaw-23006
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Frick, Thomas; Glüge, Stefan; Rahimi, Abbas; Benini, Luca; Brunschwiler, Thomas,
2021.
Explainable deep learning for medical time series data [paper].
In:
Wireless Mobile Communication and Healthcare.
International Conference on Wireless Mobile Communication and Healthcare (MobiHealth), Online, 18 December 2020.
Cham:
Springer.
pp. 244-256.
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ; 362.
Available from: https://doi.org/10.1007/978-3-030-70569-5_15
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Uwate, Yoko; Nishio, Yoshifumi; Ott, Thomas,
2021.
Synchronization of chaotic circuits with stochastically-coupled network topology.
International Journal of Bifurcation and Chaos.
31(01), pp. 2150015.
Available from: https://doi.org/10.1142/S0218127421500152
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Uwate, Yoko; Schüle, Martin; Ott, Thomas; Noshio, Yoshifumi,
2020.
Echo state network with chaos noise for time series prediction [paper].
In:
Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications.
International Symposium on Nonlinear Theory and its Applications (NOLTA), Okinawa, Japan, 16–19 November 2020.
pp. 274.
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2020.
The collaborative learning cellular automata density classification problem [paper].
In:
Proceedings of the 2020 International Symposium on Nonlinear Theory and its Applications.
International Symposium on Nonlinear Theory and its Applications (NOLTA), Okinawa, Japan, 16–19 November 2020.
pp. 268.
Current projects
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Transformernetze für Drohnensignale
Mikrodrohnen haben längst ihren Einzug in die Consumerwelt gefunden und werden immer erschwinglicher und einfacher in der Bedienung. Eine für Consumerdrohnen sehr vielversprechender Technik zur Erfassung ist die Detektion der ausgesendeten Funksignale. In diesem Projekt werden die neuartigen neuronalen Netzwerke mit ...
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Shapescience – AI for morpholigcally based fruit variety recognition
Pomologische und molekulargenetische Methoden waren bisher die Instrumente der Wahl, um Obstsorten und deren Eigenschaften zu beschreiben. Dank der Möglichkeit des Einsatzes von 3D Scanning-Technologie und maschinellen Lernens kommt es zu einem Revival des Phenotypings, also der Typisierung eines Organismus anhand ...