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The conventional schemes by using the neural networks require the information of system Jacobian to update weighting factors.
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Update weights.
Update Weight Table.
The two main phases of the algorithm are the Estimation phase (see Section "Estimation analysis") and the Update Weights phase (see Section "Updating weights").
An adaptive methodology is derived to update weight parts of the NN.
A tuning methodology is derived to update weight parts of the FNN.
A Hedge [21] strategy is used to dynamically update weights assigned to each of the classifiers.
The memetic algorithm is used to update weights which improved the accuracy of the model from 88.0 to 93.2%.
The HPAVFs with hierarchical decoupled normalized least mean square (HDNLMS) algorithms are derived to adaptively update weights of its nonlinear and linear subsections.
The training process is based on a genetic algorithm to optimize the network architecture, combined with a random search algorithm to update weight and bias values.
In order to update weights at various fire time, the time span between the input waveform and the feedback waveform should be 2t.
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