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Exact(7)

which corresponds to a PARAFAC model with matrix factors ( Φ ( 1 ), Φ ( 2 ), I I 3 ).

In [5], the effectiveness of dynamic power management in data centers had been investigated using M/M/k queuing model with matrix analytic technique.

Note that the contracted received signal tensor Y ̄ ∈ ℂ I × N × P given by (19) follows a PARAFAC model with matrix factors ( H ̄ , S, C).

Consider a third‐order tensor X ∈ C I × J × K of rank R, satisfying a PARAFAC model with matrix factors (A,B,C).

(63) It can also be viewed as a CONFAC‐(2,4) model with matrix factors (A(1),A(2),F,D), and constraint matrices Ψ(1) and Ψ(2) defined in (60).

Consider a third‐order tensor X ∈ C I × J × K satisfying a PARALIND model with matrix factors (A,B,C) and constraint matrices Φ(i), i=1,2,3.

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Similar(53)

In order to visualize the relative influence and importance of each input parameter, surface and contour plots have been produced in MATLAB by providing the ANFIS developed model with matrices of the input parameters.

This paper is concerned with gain-scheduled control of two-dimensional discrete-time linear parameter-varying systems described by a Roesser state-space model with matrices depending affinely on time-varying scheduling parameters.

This paper is concerned with the problems of robust H∞ and H2 filtering for 2-dimensional (2-D) discrete-time linear systems described by a Fornasini Marchesini second model with matrices that depend affinely on convex-bounded uncertain parameters.

The system is described by a Roesser state-space model with matrices depending affinely on time-varying parameters whose admissible values are assumed to belong to a given convex bounded polyhedral domain.

The model defined in (27) is a typical MIMO single user model with channel matrix H ~ k and precoding matrix C k.

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