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java.lang.Objectorg.encog.neural.pattern.ElmanPattern
public class ElmanPattern
This class is used to generate an Elman style recurrent neural network. This network type consists of three regular layers, an input output and hidden layer. There is also a context layer which accepts output from the hidden layer and outputs back to the hidden layer. This makes it a recurrent neural network. The Elman neural network is useful for temporal input data. The specified activation function will be used on all layers. The Elman neural network is similar to the Jordan neural network.
Constructor Summary | |
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ElmanPattern()
Create an object to generate Elman neural networks. |
Method Summary | |
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void |
addHiddenLayer(int count)
Add a hidden layer with the specified number of neurons. |
void |
clear()
Clear out any hidden neurons. |
MLMethod |
generate()
Generate the Elman neural network. |
void |
setActivationFunction(ActivationFunction activation)
Set the activation function to use on each of the layers. |
void |
setInputNeurons(int count)
Set the number of input neurons. |
void |
setOutputNeurons(int count)
Set the number of output neurons. |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public ElmanPattern()
Method Detail |
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public void addHiddenLayer(int count)
addHiddenLayer
in interface NeuralNetworkPattern
count
- The number of neurons in this hidden layer.public void clear()
clear
in interface NeuralNetworkPattern
public MLMethod generate()
generate
in interface NeuralNetworkPattern
public void setActivationFunction(ActivationFunction activation)
setActivationFunction
in interface NeuralNetworkPattern
activation
- The activation function.public void setInputNeurons(int count)
setInputNeurons
in interface NeuralNetworkPattern
count
- Neuron count.public void setOutputNeurons(int count)
setOutputNeurons
in interface NeuralNetworkPattern
count
- Neuron count.
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