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A previous study formulated the error threshold in terms of phenotypes, and their results showed that a rather low degree of mutational neutrality can increase the error threshold unlimitedly.
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The approach is based on formulating the error function in the optimization of the FIR Nyquist filter as a Lyapunov energy function to find the Hopfield related parameters.
The author extends an improved structure of feedback neural network to formulate the error function in the optimization of QMF banks as a Lyapunov energy function to find the Hopfield-related parameters.
In order to understand the performance of SCCC and the effect of design factors on the performance of the code, we need to formulate the error bounds of the SCCCs.
In this paper, first we formulate the phenotypic error threshold analytically by employing the additive assumption, in which base substitutions do not influence each other.
Unlike the conventional least-squares error minimization approach which involves fitting of a learning model to data density and then perform a threshold process for error counting, this work directly formulates the required target error count rate in terms of design model parameters with a closed-form solution.
S.G. formulated the models.
Y.A.H. formulated the hypothesis.
Thus formulated, the argument is valid.
HB formulated the hypothesis.
SK formulated the experimental diet.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com