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As it can be seen from the confusion matrices, the system demonstrates high accuracy in discrimination of human voice from other audio signals.
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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.
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.
Moreover, except the mass matrix, the system parameters and nonlinearities of the USV are all assumed to be unknown.
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.
While for a fixed matrix, the system mismatch has some potential to grow initially, it cannot keep growing and runs into a steady-state.
This new characterization exhibits a kind of decoupling between the Lyapunov matrix and the system matrices, which is subsequently exploited for control design.
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.
E and A are the coefficient matrices of the system (8), and λ is constant.
The matrices of the system state-space model are uncertain, belonging to a given polytope.
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