New element matrices need not be generated and integration is avoided completely.
New element matrices need not be generated and the singular numerical integration was avoided completely.
From conditions (9) in Theorem 2 it is clear that some constants with respect to the neuron activation functions and coefficient matrices need not be known beforehand.
Further, an adaptive strategy is introduced to design adaptive feedback controllers, where some constants with respect to system parameters including node dynamics and coupling matrices need not be calculated beforehand.
Since the domain repertoire in the human proteome is an incremental collection along its evolutionary lineage, soft matrices need not always be effective in finding all homologs.
Particularly, the weight configuration matrix is not necessarily symmetric or irreducible, and the inner coupling matrix need not be symmetric.
Moreover, the constant term (center) of the uncertain time-varying state matrix need not be diagonalizable thanks to results based on a Jordan decomposition.
Depending on the numbers of rows and columns, the requirements for optimality can be decidedly counterintuitive: replication numbers need not be as equal as possible, and trace of the information matrix need not be maximal.
It is assumed that the coupling configuration matrix need not be symmetric or irreducible and it contains state coupling, time-varying delay coupling and distributed time-varying delay coupling.
The matrix need not be formed explicitly, since only its action on the vector ({mathbf{P}}_{j(1)} (0)) is needed, i.e., a matrix-vector product, therefore Moreover, since ({mathbf{P}}_{j(1)}(0) = [{mathbf{e}}_{j}^{T} {mathbf{0}}^{T} cdots {mathbf{0}}^{T}]^{T}), then i.e., the jth column of and we get the following recurrences.
