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This paper contributes with two different prediction approaches for long-term power consumption demand prediction using an artificial neural networks (ANN) short-term time series predictor filter. The techniques proposed here are non-linear stochastic models using the energy associated to series and Bayesian inference, implemented by ANN. The system has the advantage of requiring as input only the...
The Kalman filter plays an essential role in an integrated navigation system. From an embedded-system design point of view, the UD filter is a convenient, numerically-stable version of the Kalman filter. In this paper, a UD filter coprocessor with single-precision floating-point format that runs in FPGA is presented. A comprehensive hardware/software exploration is carried out in order to find the...
In order to predict short-term times series with incomplete data, a proposed approach is presented based on the energy associated of series. A benchmark of rainfall time series and Mackay Glass (MG) samples are used. An average smoothing technique is adopted to complete the dataset. The structure of the predictor filter is changed taking into account the energy associated of the short series. The...
The Kalman filter is an effective tool for fusing signals from multiple sources. The UD filtering is a well-known, numerically-stable formulation of the Kalman filter, owing to G.J. Bierman and C. Thornton. The most popular version of this filter is oriented to be executed in a traditional, sequential microprocessor. In this paper a new algorithm for the UD filtering is presented, specially designed...
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