Sentence examples for matrix of measurement from inspiring English sources

As shown in Eq. (15), the decoder requires the covariance matrix of measurement y.

where F is the state transition matrix, H is the observation matrix, and R is the covariance matrix of measurement noise.

The covariance matrix of measurement noise, S e,noise, is estimated from the variance and covariance of measurement model differences within a 20 km2 area.

frac{{partial varvec{j} varvec{x})}}{{partial varvec{x}}} = 0 (17)where (varvec{j} varvec{x}) = Delta varvec{z}^{text{T}} varvec{R}^{ - 1} Delta varvec{z}) is the objective function; (Delta varvec{z} = varvec{z} - varvec{h} varvec{x})) is the vector of measurement residuals; (varvec{R}) is the covariance matrix of measurement error vector.

(1 where Z is the measurement vector including complex bus voltage and line current phasors; X is the state vector which is related with the measurement vector by the system matrix; I is an identity matrix; Y stands for the relationship between voltage and current; σ is a vector of the error in each of the measurements; and W is the covariance matrix of measurement errors.

Because it proved impossible to estimate a measurement model for the latent variable Knowledge alone (i.e. a not positive definite correlation matrix of measurement errors), we estimated the latent variable Knowledge and the latent variable Barriers in the same measurement model, because theoretically both play a role in the relationship between Intention and Behaviour in the ASE model.

This system can be written as L = G × X, where L is the matrix of measurements, G is the matrix of coefficients and X is the matrix of unknowns [8].

Mathematical representation of MMV model is Y=Phi X (1 where Y ∈ ℂ M × L is the matrix of measurements, Φ represents the sensing matrix, and X ∈ ℂ N × L is the row-sparse matrix which has only K nonzero rows.

The measurement error ε is typically assumed to have a normal distribution such that ε ∈ N (0, Σ η ), where Σ η is the covariance matrix of measurements.

Note that, in the Measurement Error model, matrices of measurements W and V correspond to traits X and Y, respectively.

The UIF is employed to approximate the optimum importance function due to its simplicity, by which the matrix operation is the state information matrix rather than the covariance matrix of the measurement sequence.