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The steepest descent method was used to optimize the power sharing for multiple cycles.
The Gradient Descent method was used to refine the values of the five significant parameters from the values quoted in the literature.
A standard feed-forward procedure was utilized to train the neural networks [39], and a gradient descent method was used to back-propagate the errors where-after weights were updated [40].
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Besides, for each individual of MGA, a gradient steepest descent method is used to further improve the MRL perceptron parameters supplied by MGA.
In order to improve the model accuracy, neural network technique including back-propagation and gradient descent method were used to train the fuzzy parameters to minimize the modeling error function.
In [31], a gradient descent method is used to minimize the MC of A which is described as the absolute off-diagonal elements of the Gram matrix.
82 F3 plants derived from a cross between these cultivars developed by single seed descent method were used for DNA genotyping.
In such regular scenarios, a simple gradient descent method is used to find the optimal parameter settings that maximize average user throughput while still ensuring a minimum cell-edge user throughput.
The search direction (d_{k}) is defined by d_{k} = - g_{k} + beta_{k}d_{k - 1}, quad k = 2, 3,ldots, (3) where (g_{k} = nabla f(x_{k})), (beta_{k}) is a scalar, and the steepest descent method is used as an initial search direction, i.e., d_{1} = - g_{1},quad k = 1.
However, These algorithms usually have a poor convergence rate, because the gradient descent methods is using a linear function to approximate an object function.
From F2, the single seed descent (SSD) method was used to produce the following generations.
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