10 resultados para Sarlo, Beatriz

em SAPIENTIA - Universidade do Algarve - Portugal


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Dissertação mestr., Biologia e Geologia - Educação, Universidade do Algarve, 2007

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Dissertação de Mestrado, Aquacultura e Pescas, Faculdade de Ciências do Mar e do Ambiente, Universidade do Algarve, 2006

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Despite the developments in more sophisticated controllers, still the Proportional, Integral and Derivative (PID) controller is by far the controller most widely used in industry automation.

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Neural networks and genetic algorithms have been in the past successfully applied, separately, to controller turning problems. In this paper we propose to combine its joint use, by exploiting the nonlinear mapping capabilites of neural networks to model objective functions, and to use them to supply their values to a genetic algorithm which performs on-line minimization.

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This papers describes an extantion of previous works on the subject of neural network proportional, integral and derivative (PID) autotuning. Basically, neural networks are employed to supply the three PID parameters, according to the integral of time multiplied by the absolute error (ITAE) criterion, to a standard PID controller.

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This paper describes previous works (1), (2), on neural network pid autotuning. Basically, neural networks are employed to supply PID parameters, according to the ITAE criterion, to a standard PID controller.

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The Proportional, Integral and Derivative (PID) controllers are standard building blocks for industrial automation. Their popularity comes from their rebust performance and also from their functional simplicity.

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Dissertação de mestrado, Ciências Biomédicas, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2015

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Dissertação de mestrado, Gestão de Unidades de Saúde, Faculdade de Economia, Universidade do Algarve, 2015

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Dissertação de mestrado, Ciências Farmacéuticas, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015