Alexander Schaum

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The dynamics of decisions in complex networks is studied within a Markov process framework using numerical simulations combined with mathematical insight into the process mechanisms. A mathematical discrete-time model is derived based on a set of basic assumptions on the convincing mechanisms associated to two opinions. The model is analyzed with respect to(More)
The problem of designing a globally convergent observer for a class of tubular reactors with boundary measurements is addressed. The problem is tackled by extending a dissipativity theory-based observer design for nonlinear finite-dimensional systems, which has been recently applied to a class of continuous stirred tank reactors. The underlying idea of the(More)
It is not clear so far what the implications of bifurcations in Discrete-Time Recurrent Neural Networks dynamics are with respect to learning algorithms. Previous studies discussed different phenomena in a general purpose framework, and here we are going to discuss in more detail. We perform an analysis of the dynamics of a neuron with feedback in order to(More)
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