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FWN is constructed based on the training data set of the nominal system and the constructed fuzzy rules can be adjusted by learning the translation parameters of the selected wavelets and also determining the shape of membership functions.
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Weights are adjusted by a learning process which involves the comparison of network calculations with input-output data for known cases.
In fact, as explained in the "Methods" section, the optimal value of pseudo critically depends on sequencing depth, while the parameters in the new emission functions were successfully adjusted by our learning algorithms.
The ANN parameters are adjusted by back propagation learning algorithm using 285 collected experimental data sets from various literatures.
The hyperparameters (the regularization constant C and Gaussian kernel width) have been adjusted by repeating the learning experiments for the set of their predefined values and choosing the best one for the validation data set.
The parameters of the ANN model are adjusted by the back propagation learning algorithm using wide ranges of experimental datasets.
The parallel fiber - Purkinje cell connections, whose weights were adjusted by means of a learning process, represented the main learning sites of this neural network.
In the model, the learning weights are adjusted by the proposed anxious confident decayed brain emotional learning rules (ACDBEL).
By forming these relationships in advance, large organizations demonstrate sincere interest in issues they are likely to encounter in the future, or in the best case scenario, are able to avoid them altogether by learning and adjusting their culture well before a crisis is upon them.
For instance, they learn to adjust their own workplace by themselves which is in contrast to general occupational health management, where workplaces are often adjusted by occupational therapists.
Medium osmolarity was adjusted by adding NaCl.
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