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Remarkably, the range element of the correlation term is so significant that it is several times larger than the range variance of the second-order term (SOT) and four orders of magnitude larger than the variance of the range measurement.
The inferred parameters are then used as multiplicative coefficients for each element of the correlation matrix, so that we obtain a weighted correlation matrix, reflecting the physical interaction in the network.
This procedure makes null the element of the correlation matrix if the rate constant multiplying this element is a null kinetic rate constant or if this rate constant is affected by a relative error of 50%.
The ijth element of the correlation matrix R can then be calculated as R i j = V i j V i i V j j Simplified formulas for two special cases (binary and trinomial variables), using 2 × 2 and 3 × 3 contingency tables, can be found in Okada et al. [ 30, 33].
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According to Figure 1, the diagonal elements of the correlation matrix trend to 1, and others are very small.
Finding the optimal weights for, using standard least-squares fitting, requires a well-conditioned invertible matrix, which we ensure using a custom-designed regularization technique of adding a small fraction to the diagonal elements of the correlation matrix.
The first column in Table 1 represents the elements of the correlation matrix C. It is observed from Table 1 that the correlation between gas and electric loads has no discernible impact on the mean value of the total operational costs, while correlation has a more significant impact on their standard deviation.
Figure 4 shows that as the diagonal elements of the correlation matrix (variance of each sensor's observation) get close to each other, the performance of the mGLRT degrades until it matches the optimal independent test when the variance of the sensor observations is equal.
More specifically, the elements of the correlation matrix for stationary phase noise are given by Φ ϕ ( m − n ) ϕ ( m − n ) = Π θ rms 180 2 e − 2 Π Ω 0 | m − n | T s, where Ω 0=1000 kHz is the 3-dB bandwidth of a single pole Butterworth filter.
However, neglecting the off-diagonal elements of the correlation matrix and using only the diagonal elements 1 - α σ v r 2 of the correlation matrix, the steady-state MSE of the proposed GAL narrowband ANC system can be approximated as E e 2 n = ∑ i = 1 I σ d i 2 1 + M ξ i 2 1 - α 2 + ∑ i = 1 I M ξ i σ d i 2 2 1 - α 1 + M ξ i 2 1 - α 2 + σ v p 2. (15).
Ridge regression stabilizes model parameter estimates by multiplying a contant (i.e., Ridge k) to the elements of the correlation matrix, displacing it from singularity and statistically compensating for the effects of multicollinearity.
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