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When EBV have a variety of reliabilities then the regression can be weighted accordingly so that, where R is a diagonal matrix of weights.
In particular, the algorithm called 'single convolution algorithm' introduces significant advantages: a more accurate measurement of the level of motion using a matrix of weights, and a unique fuzzification process after the global estimation, which reduces the computational cost.
Given a matrix of weights, the linear ordering problem (LOP) consists of finding a permutation of the columns and rows in order to maximize the sum of the weights in the upper triangle.
Where D is a matrix of weights, and I is an identity matrix.
The matrix of weights (hat{mathbf{D}}) obtained from the algorithm can be further decomposed to extract the signal (u_{1}) (up to a sign) as follows.
The filter output can be analyzed as the product of a matrix of weights W with the vector of the given the input image y.
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In this way, we build the straight reciprocal matrix of weight by comparing the influence to data transmission of any two of the influence factors.
where is a matrix of weight vectors with columns, is a vector of class biases, and is a probability normalization term.
R = Λ × E M T Λ = X × C T with EM = { em i} a nr × nem matrix of nem EFM, em i (dim emi) = q), Λ = { λ(t)} a np × nem matrix of weight vectors λ(t) of EFM (dim = nem) and C a nem × nx matrix of regression coefficients.
The coefficients of the matrices of weights and biases are detailed in appendix of Salameh (2016).
Table 4 shows the matrices of weights between input, hidden and output layers.
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