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The algorithm is defined in an evolving dynamic environment with semiautonomous neurofuzzy agents.
The algorithm is defined using the linear summation of input patterns and their randomized input weights.
From a global (network) point of view, the algorithm is defined in Algorithm 1.
The algorithm is defined such that it explicitly deals with the constraints imposed by a consistent solution executor.
The stopping condition for the algorithm is defined according to the maximum number of generations or a satisfactory fitness level.
The algorithm is defined as follows: S i k = max F i k − 1, v a i k, F i k = S i k + L i k ϕ i, (5).
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The control parameters of the algorithm are defined in the data file that uses the INI-format.
In the case of a depth-first search algorithm [13], the first solution given by this algorithm is defined as the Babai point [25, 26].
In Section 5, the channel model and channel coefficient distribution are introduced, and the BASSAMP algorithm is defined for the RFID scenario.
The initial conditions are x 0 0 = μ 0 and P 0 0 = Σ 0. The interval Kalman smoother, which is needed for the EM algorithm, is defined by (8).
First, assume that the terminated point of sub-algorithm is (x_{sup}), then the given algorithm is defined as follows.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com