Sentence examples for matrices of state from inspiring English sources

This paper concerns the robust H∞ filtering problem for discrete-time singular systems with norm-bounded uncertainties in all system matrices of state equations.

Using classical feedback control principle the LMI-based procedure is provided for computation of the gain matrix of state control laws, and influence of equality constraints is explained.

In addition, the extraction of equivalent system matrix of state equation of motion for structures from the transformed VFAR model has been developed, and then the normal modes can be calculated from the identified equivalent system matrix.

The resulting M ×M matrix of state transition probabilities (denoted by P a ) can be translated into a corresponding infinitesimal generating matrix in the continuous-time domain, given by B a =(1/τa)(P a −I M ), where I M is an identity matrix.

The equation to be solved with the FSP approximation is (10) d dt P (t ) = A P (t ) for t ∈ (0, T ) P (0 ) = P 0, where A ∈ R d × d is the matrix of state transition propensities, and P 0 = p 0 (x (i ) ) i = 1, …, d is a vector of initial probabilities for the states in Ω.

F and G are the state transition matrices of the state vector and the process noise gain matrix.

In filter design, an upper bound filter is explored to compute, recursively and adaptively, the upper bounds of covariance matrices of the state prediction error, innovation and state estimate error.

Adopting transfer matrices, continuity of state variables at interfaces of segments establishes the global dynamic equilibrium of the segmented cylinder.

Differential evolution is modified by taking into account the covariance matrix of states.

The gain matrices of the state estimator can be then found by solving a set of coupled linear matrix inequalities.

The novel off-line technique for estimation of the covariance matrices of the state and measurement noises is designed.