Ewaldo Santana

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Introduction Diabetes patients can benefit significantly from early diagnosis. Thus, accurate automated screening is becoming increasingly important due to the wide spread of that disease. Previous studies in automated screening have found a maximum accuracy of 92.6%. Methods: This work proposes a classification methodology based on efficient coding of the(More)
The proposed methodology is based on development of online algorithms for approximate solutions of the Hamilton-Jacobi-Bellman (HJB) equation through a family of non-squares approximators for critic adaptive solution of the Discrete Algebraic Riccati Equation (DARE), associated with the problem of Discrete Linear Quadratic Regulator (DLQR). The proposed(More)
Machine learning algorithms are used in many areas, in signal processing, the adaptive filtering has been used in many jobs as smooth, prediction, equalization, etc. The Least Mean Square (LMS) algorithm is a successful example of this approach, this algorithm takes the instantaneous gradient of the cost function in his learning process. Nevertheless,(More)
In this paper, a method to design online optimal policies that encompasses Hamilton-Jacobi-Bellman (HJB) equation solution approximation and heuristic dynamic programming (HDP) approach is proposed. Recursive least squares (RLS) algorithms are developed to approximate the HJB equation solution that is supported by a sequence of greedy policies. The proposal(More)
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