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In contrast, the shrinkage estimates of the covariance matrices are guaranteed to be positive definite and hence invertible.
Positive semidefiniteness of the linear combination of kernel matrices is guaranteed by constraining the weights of the kernels to be non-negative.
The equality of polynomial matrix and dft matrix decomposed matrices decompositions are guaranteed via the finite duration constraint, which is investigated in Section 4. The finite duration constraint imposes the phase angles of singular vector (eigenvector) to minimize a nonlinear quadratic function.
It implies that any K sub-matrices of A are guaranteed to yield a set of linearly independent columns so that k A ″ ≥ K. On the other hand, define A ̂ as A ̂ = A 1, …, A K, A K + 1.
Then according to the Lyapunov direct method and the decentralized control theory of large-scale interconnected systems, linear matrix inequality (LMI -based conditions with some free weighting matrices are derived, which guarantee the cLMI -based interconditionssystems to be robustly stable with the H∞ performance even in the presomee ofreeme possible actuator failures.
Formulated by Equation (5), coefficient matrix K is guaranteed to accomplish the convolution operation without approximation.
The resulting stability conditions are formulated in the form of linear matrix inequalities (LMIs) and by solving the obtained LMIs, desired impulsive control gain matrices are calculated, which are capable to guarantee the exponential stability of error system.
Results are guaranteed.
So fireworks are guaranteed.
Surprises are guaranteed.
However, my decision matrices are not fancy.
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