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Moreover, except the mass matrix, the system parameters and nonlinearities of the USV are all assumed to be unknown.
While for a fixed matrix, the system mismatch has some potential to grow initially, it cannot keep growing and runs into a steady-state.
Namely, the estimation algorithms use the observation matrix, the system matrix for the state variable and the crossvariance function between the state variable and the signal.
By making the pairwise similarities available in the matrix, the system does not have to repeat the similarity calculation when the similarity of the same pair of images is required again in the iterative clustering process.
The estimators require the information of the observation matrix, the system matrix for the state variable, related with the signal, the cross-variance function of the state variable with the observed value and the variance of the white observation noise.
As the time advances by the time integration, since the system matrix is the function of nonlinear modal stiffness matrix, the system matrix also is modified after each iteration within each time step.
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By introducing extra slack variables, the Lyapunov matrix and the system matrix are decoupled such that the controller parametrization is independent of the Lyapunov matrix.
This new characterization exhibits a kind of decoupling between the Lyapunov matrix and the system matrices, which is subsequently exploited for control design.
As it can be seen from the confusion matrices, the system demonstrates high accuracy in discrimination of human voice from other audio signals.
Third, dilated linear matrix inequalities are used to decouple the Lyapunov matrices from the system matrices.
Sufficient conditions are first established via introducing some slack matrices to facilitate the FDF design procedure by eliminating the coupling between the Lyapunov matrices and the system matrices.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com