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Fig. 5 | EURASIP Journal on Advances in Signal Processing

Fig. 5

From: Optimal measurement budget allocation for Kalman prediction over a finite time horizon by genetic algorithms

Fig. 5

Outcomes of the experiment described in Section 3.2.2. a Eigenvalues of the randomly sampled matrix A. b Histogram of the benefit \(\mathcal {B}= \text {MSE}(\mathcal {M}_{\text {REG}}) - \text {MSE}(\mathcal {M}_{\text {GA}})\) computed over 100,000 realizations for the problem described in Section 3.2.2. The histogram approximates the probability density function of the benefit. The red vertical line indicates a null benefit. The mean and standard deviation of the benefit are 6085.12Ā±3391.64 and the benefit is positive for 97% of the realizations

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