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The network is constructed by the error backpropagation neural network using Morlet mother wavelet basic function as node activation function.
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The error backpropagation algorithm was used for an ANN training procedure.
We illustrate these concepts by the analysis of the Hebbian and Widrow Hoff rules and some basic neural network architectures (i.e., the linear autoassociator, the linear heteroassociator, and the error backpropagation network).
The error backpropagation and weight modification step is calculated for all hidden layers of the neural network away from the output layer to the input layer.
Figure 3 shows how the BPPNN works, starting from structure of the system, normalization, weight initialization, feed-forwarding, computation of the error, backpropagation, updating the weights, and testing the fixed model.
The error-backpropagation algorithm is used to train the ANN model.
The Jacobian matrix can be computed from a modified version of the error-backpropagation algorithm, which is explained in [27].
The fine-tuning of the DAE was performed by error backpropagation with squared error as the loss function.
This paper presents a rheological parameter estimation technique by using error backpropagation neural network (BN) and genetic algorithm (GA).
The hybrid learning algorithm of the modified ANFIS comprises two steps, i.e. forward pass by using fuzzy inference mechanism based on the neural architecture and Least-squares Estimator (LSE), and backward pass to update the premise parameters by the error a modified backpropagation algorithm.
Nodes in the hidden layers are partially trained by three cycles of error backpropagation: training the nodes associated with speech act identification, predicator identification, and sentiment identification.
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