Kronecker

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Kro·nec·ker

(krō'nek-ĕr),

Karl H., Swiss physiologist, 1839-1914. See: Kronecker stain.

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NOMENCLATURE k scan time index j system model index [[bar.x].sub.k] system state vector [y.sub.k] measurement vector [A.sub.j] state transition matrix [B.sub.j] input matrix f(*) measurement model [R.sup.j] process noise covariance matrix [M.sup.j] measurement noise covariance matrix N covariance matrix of [n.sub.k] S set of all models [v.sup.j.sub.k] process noise vector [[omega].sup.j.sub.k] measurement noise vector [[delta].sub.k(k-i)] Kronecker delta function [[??].sup.j.sub.k] system model at kth step [[pi].sub.ij] transitions probability from ith model to jth model [phi] initial state distribution of Markov chain [[mu].sub.k] adversary steering command vector [[mu].sup.j.sub.k] mode probability Digital Object Identifier10.4316/AECE.2017.03005

A novel robust interacting multiple model algorithm for maneuvering target tracking

The Kronecker endomorphism I [member of] [GAMMA](End([pi])) = End([GAMMA]([pi])) acts locally as:

Conjugate covariant derivatives on vector bundles and duality

If the D-dimensional sample signal X = [[x.sup.T.sub.1], [x.sup.T.sub.2], ..., [x.sup.T.sub.D]] and independent measurements result Y = [[y.sup.T.sub.1], [y.sup.T.sub.2], ..., [y.sup.T.sub.D]] are unknown, the Kronecker product measurement matrix [5] can be expressed as [bar.[PHI]] = [[PHI].sub.1] [cross product] [[PHI].sub.2] [cross product] ...

A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing

Huang, "Novel synchronization analysis for complex networks with hybrid coupling by handling multitude Kronecker product terms," Neurocomputing, vol.

Pinning Synchronization for Complex Networks with Interval Coupling Delay by Variable Subintervals Method and Finsler's Lemma

The reader is referred to [23] for other properties of the Kronecker product not mentioned here.

Generalized Characteristic Polynomials of Join Graphs and Their Applications

The robust stabilization in the paper [9] (which published the same results as can be found here) was based on the similar idea as in the contribution [5], but it used the alternative methods, namely the combination of the Kronecker summation method [10] and the algebraic approach to control design under the ring of proper and stable rational functions ([R.sub.PS]) [11], [12], [13], [14], [15].

Robust Stabilization of a Continuous Stirred tank Reactor Model

where [G.sub.d] is the (co)variance matrix of random direct additive genetic effects; [G.sub.m] is the (co) variance matrix of random maternal additive genetic effects; Q is the (co)variance matrix of random permanent maternal environment effects; R is the (co)variance matrix of random residual effects, A is the numerator relationship matrix, [I.sub.v] is the identity matrix with order v, where v is the number of mothers, [I.sub.n] is the identity matrix with order n, where n is the number of observations, and [R] is the Kronecker product operator.

Multi-trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle/Efetividade da reducao da dimensao da matriz de covariancia do efeito genetico direto na avaliacao genetica do crescimento em bovinos Simental

[f.sub.m]([x.sub.n]) = [[delta].sub.m,n] (the Kronecker symbol) m, n [member of] N.

A criterion of strong density of operator subalgebras

G is considered as the genetic covariance matrix of the random regression coefficients assumed to be the same for all cows; A is considered as the additive genetic relationship matrix among all animals; is the Kronecker product operator; P is the permanent environment covariance matrix of the random regression coefficients assumed to be the same for all cows; residual covariance matrix was equal to (Eq.) in which I is an identity matrix, and the residual variance was assumed to be constant throughout the lactation.

GENETIC EVALUATION OF LACTATION PERSISTENCY AND MILK YIELD IN IRANIAN HOLSTEIN DAIRY CATTLE USING RANDOM REGRESSION MODELS

In case if the users equipped with the same number of receive antennas, then the combining strategy is simply the Kronecker product between [T.sup.(i).sub.u] and [T.sup.(j).sub.sk].

Centralised and decentralised precoding framework in multi user-mimo wireless communication

In the twelfth chapter on Graph Generation by Kronecker Multiplication, the authors have provided a brief description to offset the unequal distribution of adjacency matrix followed by a summary in the next chapter.