Exact(3)
The auto-covariance function of the signal is factorized in terms of the observation vector, the system matrix and the cross-variance function of the state variable, that generates the signal, with the signal.
where x T ( t ) = [ x 1 T ( t ), …, x N T ( t ) ], x i ( t ) ∈ R n i, is the state vector, the system matrices A i, D i j are of appropriate dimensions.
where ⊥ denotes orthogonality, Γ R = [ γ1,γ2] T, and Γ 0 = [ h 1 D, h 2 D ] T / ∑ m = 1 2 | h mD | 2. Because of the orthogonality feature of the network coding vector, the system sum rate capacity in the network-coded cooperative mode can be obtained directly from [7] C NCCO = log 1 + ρ ∑ m = 1 2 | h mD | 2 1 + ρ | h RD | 2. (15).
Similar(57)
where, and is the state vector and input vector of the system, respectively.
As an alternative for the vector construction, the system can also produce a binary sentiment vector.
Vector in (2.2) is thus not a state vector of the system.
Input vector q (order m × 1) is a hypothetical random-noise source vector perturbing the system.
Here, the n denotes the noise vector of the system.
where nsystem denotes the noise vector of the system.
A pure state is represented by a vector in the system's Hilbert space.
Its solution yields the probability density vector of the system at each point in time.
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