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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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The neural network is trained using a backpropagation algorithm, and the connection weights along with the network parameters are adjusted during this process.
weka-MultilayerPerceptron+Back_Propagation: a neural network that uses a backpropagation algorithm to classify instances.
They are all implemented in WEKA: weka-MultilayerPerceptron+Back_Propagation: a neural network that uses a backpropagation algorithm to classify instances.
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.
A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed.
Using the collected data set and LCC analysis results for training, a three-layer feedforward ANN model based on a backpropagation algorithm was developed.
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.
After pre-training, a backpropagation algorithm was applied to adjust the parameters.
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