Math

Browse all posts in the Math category.

67 to 72 of 82

16DEC 2020
Math
Model-based optimization of ripening processes with feedback modules
Model-based optimization of ripening processes with feedback modules Michele Spinola, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg 1 Important remark This contribution presents a proof of concept together with numerical results to obtain a first idea how to deal with specific process chains within chemical engineering. The main reference of this webpage entry is [1]. Furthermore, for a […]Model-based optimization of ripening processes with feedback modules Michele Spinola, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg 1 Important remark This contribution presents a proof of concept together with numerical results to obtain a first idea how to deal with specific process chains within chemical engineering. The main reference of this webpage entry is [1]. Furthermore, for a […]
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23NOV 2020
Math
Gas networks uncertainty and Probust constraints: model, distribution and optimization
Gas networks uncertainty and Probust constraints: model, distribution and optimization Martin Gugat, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg Gas transport and distribution systems are usually operating under complex pipelines network topologies which make possible gas flow over interconnected stations -nodes- and branches under a variety of conditions, especially large-scale gas infrastructures. As many applications contain different types […]Gas networks uncertainty and Probust constraints: model, distribution and optimization Martin Gugat, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg Gas transport and distribution systems are usually operating under complex pipelines network topologies which make possible gas flow over interconnected stations -nodes- and branches under a variety of conditions, especially large-scale gas infrastructures. As many applications contain different types […]
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04NOV 2020
Math
Q-learning for finite-dimensional problems
Q-learning for finite-dimensional problems Carlos Esteve Yagüe, Deusto CCM Reinforcement Learning Reinforcement Learning (RL) is, together with Supervised Learning and Unsupervised Learning, one of the three fundamental learning paradigms in Machine Learning. The goal in RL is to enhance the manipulation of a controlled system by using data from past experiments. It differs from […]
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13OCT 2020
Math
The interplay of control and deep learning
The interplay of control and Deep Learning Borjan Geshkovski, DeustoCCM It is superﬂuous to state the impact deep (machine) learning has had on modern technology, as it powers many tools of modern society, ranging from web searches to content ﬁltering on social networks. It is also increasingly present in consumer products such as cameras, […]
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01OCT 2020
Math
Neural networks and Machine Learning
Neural networks and Machine Learning Marius Yamakou, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg Neural Networks with time delayed connections Neurons communicate with each other through electrical signals. It is well known that these signals are oscillatory and that the properties of the oscillations depend on the characteristics of the individual neurons, how the neurons are connected, and […]Neural networks and Machine Learning Marius Yamakou, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg Neural Networks with time delayed connections Neurons communicate with each other through electrical signals. It is well known that these signals are oscillatory and that the properties of the oscillations depend on the characteristics of the individual neurons, how the neurons are connected, and […]
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23SEP 2020
Math
Stochastic Synchronization of Chaotic Neurons
Stochastic Synchronization of Chaotic Neurons Marius Yamakou, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg Real biological neurons can show chaotic dynamics when excited by the certain external input current. The behavior of these neurons is characterized by instability and, as a result, limited predictability in time. Mathematically, a system is chaotic if it has a long-term aperiodic […]Stochastic Synchronization of Chaotic Neurons Marius Yamakou, FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg Real biological neurons can show chaotic dynamics when excited by the certain external input current. The behavior of these neurons is characterized by instability and, as a result, limited predictability in time. Mathematically, a system is chaotic if it has a long-term aperiodic […]
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