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Additionally, the formulated model is locally refined by utilizing the prediction error, which is inherently obtained in H-PCFE.
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Combined the fuzzy PDA algorithm, the IMM fuzzy probabilistic data association algorithm (IMM-FPDA) proposed in [24] utilizes the prediction error and its change as the fuzzy inputs to optimize parameters of the fuzzy system, and the fuzzy system is used to determine the process noise covariance of the Kalman filter.
If the predicted value is regarded as a deterministic value and the prediction error is utilized to describe the fuzziness of renewables, only the spinning reserve constraint should be considered.
In this section we utilize the bias-variance decomposition of the prediction error and show when and how regularization can lead to smaller prediction error.
Predictions have smaller variance than the variable predicted when the prediction error is not zero.
In order to compare, the prediction error curve of the method utilized by Saintigny et al. [ 3] to select effective genes named component-wise likelihood-based boosting was also provided in Figure 2. As shown, based on prediction error curve, the performance of SCAD was better compared to component-wise likelihood-based boosting.
To estimate the prediction error we use the.632+ estimator [ 24].
In order to shape the prediction error for higher performance, the prediction error preprocessor utilizes the JND profiles to process the prediction error signals such that the dynamic range of processed prediction error signals can be reduced to achieve lower bit rate or better reconstructed image quality.
Similarly, for data set-2, nearly 63% improvement in the prediction error was observed when the interferent information was utilized in Spectral Interferent Subtraction preprocessing.
The prediction error was <10%.
The prediction error interval is then ((0,p_s)).
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