Setting the extruder operating conditions, or establishing the adequate geometry of the screw, for a given group of specifications, is dealt with as an optimization problem where the solution is searched in a nonconvex space. In practice, this is a conflicting multi-attribute optimization problem, where various optima may coexist. The methodology developed involves the maximization of a n objective function, quanbfymg the adequacy of the extruder-die combination response to particular inputs, whose values are computed using an extrusion modeling package developed for this work. Genetic Algorithms (GAS) are used to generate better sets of inputs. In a second stage, multi-objective optimization through Pareto curves, coupled to GAS, is implemented since, beyond optimization, it also provides a better understanding of the characteristics of the extrusion system under study.
Abstract. Liver diseases have severe patients' consequences, being one of the main causes of premature death. These facts reveal the centrality of one`s daily habits, and how important it is the early diagnosis of these kind of illnesses, not only to the patients themselves, but also to the society in general. Therefore, this work will focus on the development of a diagnosis support system to these kind of maladies, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks.
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