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)
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)
Hyperspectral detection theory has spawned a new signal processing framework called Continuum Fusion, which integrates infinitely many optimal algorithms, in problems where a lack of prior knowledge precludes selecting the correct one. The methodology can generate several varieties of algorithm for any given model, depending on the constraints imposed… (More)
By exploiting human insight in the form of a model, methods of composite hypothesis (CH) testing can generate more robust decision algorithms, with a greater ability to generalize, than the alternative “data-driven methods.” The latter include artificial neural networks, genetic algorithms, support vector machines, etc.
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)