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The convergence of AMP-CTLS is analyzed, and the initial conditions of the algorithm are discussed.
Although the algorithm is designed as an unkeyed hash function, the control parameters and the initial conditions of the algorithm can be treated as the secrete key for a keyed hash function.
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These results should be considered with caution as applying inference models is conditioned on the algorithm of reconstruction: simulated annealing in Banjo and forward selection for the integral additive model.
The alternation of the two definitions depends on the termination condition of the algorithm.
In this article, following scheme is suggested when utilize MCN as termination condition of the algorithm.
We estimate an error upper bound, also we drive the stability condition of the algorithm.
The discrete optimization algorithm is described as shown in Algorithm 1. Algorithm 1. Initialization:, ; ; while %Performing condition of the algorithm.
According to the characteristics of the systems, a closed-loop PD-type learning algorithm is proposed and the convergence condition of the algorithm is established.
Then a closed-loop PD-type learning algorithm is adopted for such singular systems, and the convergence condition of the algorithm is established.
Our stopping condition of the algorithm required that the sum of the absolute values of all cross terms must be less than.
The Laplacian operator is used to represent the interconnected filter structure as a compact error dynamic for deriving the convergence condition of the algorithm.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com