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The state-transition matrix is used to find the solution to a general state-space representation of a linear system in the following form ˙ = () + (), =, where () are the states of the system, () is the input signal, () and () are matrix functions, and is the initial condition at .
If the Markov chain is time-homogeneous, then the transition matrix P is the same after each step, so the k-step transition probability can be computed as the k-th power of the transition matrix, P k. If the Markov chain is irreducible and aperiodic, then there is a unique stationary distribution π. [41]
In the state-transition table, all possible inputs to the finite-state machine are enumerated across the columns of the table, while all possible states are enumerated across the rows. If the machine is in the state S 1 (the first row) and receives an input of 1 (second column), the machine will stay in the state S 1.
The fundamental matrix is used to express the state-transition matrix, an essential component in the solution of a system of linear ordinary differential equations. [3]
In quantum physics, Fermi's golden rule is a formula that describes the transition rate (the probability of a transition per unit time) from one energy eigenstate of a quantum system to a group of energy eigenstates in a continuum, as a result of a weak perturbation.
The state-transition equation is defined as the solution of the linear homogeneous state equation. The linear time-invariant state equation given by = + + (), with state vector x, control vector u, vector w of additive disturbances, and fixed matrices A, B, E can be solved by using either the classical method of solving linear differential equations or the Laplace transform method.
a transition relation R ⊆ S × S such that R is left-total, i.e., ∀s ∈ S ∃s' ∈ S such that (s,s') ∈ R. a labeling (or interpretation) function L: S → 2 AP. Since R is left-total, it is always possible to construct an infinite path through the Kripke structure. A deadlock state can be
This is the opposite of what one might expect, but is appropriate for conventional matrix multiplication. For each state k, the increase in occupation probability depends on the contribution from all other states to k, and is given by: , where is the probability for the system to be in the state , while the matrix is filled with a grid of ...