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The MLP is trained by a backpropagation algorithm from random initial parameters.
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Normally a backpropagation algorithm is applied to learn the weights in the network.
After pre-training, a backpropagation algorithm was applied to adjust the parameters of autoencoder.
weka-MultilayerPerceptron+Back_Propagation: a neural network that uses a backpropagation algorithm to classify instances.
After pre-training, a backpropagation algorithm was applied to adjust the parameters.
A backpropagation algorithm is used to train both the FNNI and FNNC on line.
A backpropagation algorithm with momentum was used to prepare the neural network.
Nevertheless, we used the same configuration proposed by Quintero, Lopez, and Cuervo [14] – a two-layer neural network, with nine neurons in the intermediate layer, 31 inputs, and trained with a backpropagation algorithm.
They are all implemented in WEKA: weka-MultilayerPerceptron+Back_Propagation: a neural network that uses a backpropagation algorithm to classify instances.
A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed.
The authors used a backpropagation algorithm, and the best performing architecture was a two-layer neural network, with nine neurons in the intermediate layer and 31 inputs.
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