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Using this probabilistic approach, different subsets of the CIs extension are identified to meet predefined constraints (identification phase) and eventually selected for realization to improve a given merit function (selection phase).
The Fractal Flames algorithm involves random function selection that needs to be calculated in each thread.
This includes: Genotype Population initialization Fitness function Selection operator Crossover operator Mutation operator.
Applying a presorting algorithm that removes the random function selection and eliminates branch divergence completely does this.
Furthermore, kernel function selection and parameters settings which are used for SVM method are evaluated and discussed.
Another property of the MLP hidden layers that should be determined in the modeling process is transfer function selection.
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To determine function selections and proper weights, genetic algorithm is employed for optimization.
The radial basis functions selection is not decisive for the network efficiency.
This size reduction process can be mathematically represented (in simplified form) by two major functions: selection function (breakage probability) and breakage function.
This basis functions selection relies on a priori knowledge about the target signal, which is always infeasible in engineering applications.
Guidance for setting the feedback configuration for loop shaping, weighing functions selection and synthesis are also presented.
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