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Table 2 Results for the spring-mass system (Section 3.2.1) and the random system (Section 3.2.2). The cost using regularly spaced measurements and the optimal cost found by the GA are indicated. The mean square errors are indicated (mean and standard deviation over 100,000 realizations). The benefit \(\mathcal {B}= \text {MSE}(\mathcal {M}_{\text {REG}}) - \text {MSE}(\mathcal {M}_{\text {GA}})\) is also indicated (mean, standard deviation and proportion of positive)

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

Ā 

Spring-mass system

Random system

Cost regular

0.52

16,817.80

Cost GA

0.39

10,621.31

\(\text {MSE}(\mathcal {M}_{\text {REG}})\) (mean Ā± std)

0.51Ā±0.42

16,506.12Ā±3212.83

\(\text {MSE}(\mathcal {M}_{\text {GA}})\) (mean Ā± std)

0.39Ā±0.33

10,421.00Ā±1866.53

Benefit \(\mathcal {B}\) (mean Ā± std)

0.12Ā±0.38

6085.12Ā±3391.64

Proportion of positive benefit \(\mathcal {B}\)

64%

97%