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Applying the fundamental solution matrix of coefficient matrix, we obtained a series of new sufficient conditions to guarantee the existence and global exponential stability of an anti-periodic solution for the BAM neural networks with time-varying delays in the leakage terms.
From each matrix, we obtained 10 parameters commonly used to describe network structure.
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Using information of the eigenvalues of the adjacency matrix, we obtain necessary conditions for existence.
Considering the unitarity of WFRFT matrix, we obtain (E|mathbf {d}_{alpha }|_{2}^{2}=E|mathbf {d}|_{2}^{2}).
From this matrix we obtain the magnetic energy distribution in the wave vector domain.
Taking conjugation (Q^{-1} (cdot) Q) by the matrix we obtain the second part of our assertion.
Therefore, there exists a positive constant such that and from the properties of matrix, we obtain (4.11).
Together with the local phase matrix, we obtain a certain wave transmitting from a layer to the neighboring one.
Applying the item (b) of Proposition 3 to this matrix we obtain ρ ( A + B Δ ) = ρ ( A ˆ + B ˆ Δ ˆ ) < 1 if and only if δ < | I − A | b.
By properties on the strictly diagonally dominant matrix and the normal matrix, we obtain rho(T leq|T|_{2}leq frac{1}{2} bigl[ rho I-gamma G_{1})+rho I-gammaa G_{2}) bigr]< 1} (28) where (rho(T)), (rho(I-gamma G_{1})) and (rho(I-gamma G_{2})) are the spectral radii of the matrices T, ((I-gamma G_{1})) and ((I-gamma G_{2})).
After redefining the ordering matrix, we obtain a sequence of transition matrices T q corresponding to Y q = { ( y q 2 i − 1 ) : ( y i ) ∈ Y } for q = 2 k + 1, k ≥ 0. The following theorem exhibits the computation of Γ n ( Y ) and h ( Y ).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com