Altmann, S, Silva MS.
2013. Metafísica e Ciência. Diálogos com a escola: experiências em formação continuada em filosofia na UFRGS. (SPINELLI, P; PORTO, L.S.; ZANUZZI, I.; SANTOS, R.B., Ed.).:113-150., Porto Alegre: Evangraf Abstract
Borba, MC, Soares DS.
2012. Modeling in Brazil: A case involving Biology. In: Blum, W.; Borromeo Ferri, R.; Maass, K. (Org.) Mathematikunterricht im Kontext von Realität, Kultur und Lehrerprofessionalität. , Berlim: Springer
This work solves an experiment design problem for a linear regression problem using a reduced order model. The quality of the model is assessed using a mean square error measure that depends linearly on the parameters. The designed input signal ensures a predefined quality of the model while minimizing the input energy.
Bailão, EFLC, Parente AFA, Parente JA, Silva-Bailão MG, De Castro KP, Kmetzsch L, Staats CC, Schrank A, Vainstein MH, Borges CL, Bailão AM, De Almeida Soares CM.
2012. Metal acquisition and homeostasis in fungi. Current Fungal Infection Reports. 6:257–266., Number 4 AbstractWebsite
This paper studies a one-shot (non-iterative) data-based method for Model Reference (MR) control design. It shows that the optimal controller can be obtained as the solution of a Prediction Error (PE) identification problem that directly estimates the controller parameters through a reparametrization of the input-output model. The standard tools of PE Identification can thus be used to analyze the statistical properties (bias and variance) of the estimated controller. It also shows that, for MR control design, direct and indirect data-based methods are essentially equivalent.
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