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Such transition matrices can represent stochastic processes and thus facilitate modelling uncertainty in rotations, and forecasting of the long-term proportions of each crop in a rotation, such as changes in climate or economics.
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Because rotation matrix can represent the set of attitudes both globally and uniquely, the two controllers can deal with unwinding that can result in extra fuel consumption.
Nevertheless, it has been concurrently shown not only that the covariance matrix can represent a characteristic fingerprint for a physiological state of an investigated system, but also that it changes with steady-state concentrations of metabolites in mutant organisms [29] and reflects such complex processes such as an increase in anaerobic respiration in hepatic tumor [30].
After being normalized so that the entries sum to one, this matrix can represent the co-occurrence probabilities between the classes and latent components.
PPI adjacency matrices can be represented as graphs whose nodes represent proteins and edges represent interactions.
The linear state space matrices can be represented as loosely coupled first and second order sections.
Mass and stiffness matrices can be represented systematically by overallgeometry and material characteristics.
The resulting sharing matrices can be represented as two collections of n × m Boolean matrices S = {S0,S1}.
In addition, the expected finite-time state observer gain matrices can be represented by a feasible solution of the set of LMIs.
By the use of the singular value decomposition (SVD) the channel matrix can be represented as H=U Σ V H, where V H represents the conjugate transpose of V.
The hybrid similarity matrix can be represented as a weighted graph whose nodes represent the edges in the summary graph and the weight between two nodes corresponds to the hybrid similarity between the corresponding two edges.
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