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java.lang.Objectorg.encog.neural.pattern.JordanPattern
public class JordanPattern
This class is used to generate an Jordan 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 output layer and outputs back to the hidden layer. This makes it a recurrent neural network. The Jordan neural network is useful for temporal input data. The specified activation function will be used on all layers. The Jordan neural network is similar to the Elman neural network.
| Constructor Summary | |
|---|---|
JordanPattern()
Construct an object to create a Jordan type neural network. |
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| Method Summary | |
|---|---|
void |
addHiddenLayer(int count)
Add a hidden layer, there should be only one. |
void |
clear()
Clear out any hidden neurons. |
MLMethod |
generate()
Generate a Jordan 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 |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
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public JordanPattern()
| Method Detail |
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public final void addHiddenLayer(int count)
addHiddenLayer in interface NeuralNetworkPatterncount - The number of neurons in this hidden layer.public final void clear()
clear in interface NeuralNetworkPatternpublic final MLMethod generate()
generate in interface NeuralNetworkPatternpublic final void setActivationFunction(ActivationFunction activation)
setActivationFunction in interface NeuralNetworkPatternactivation - The activation function.public final void setInputNeurons(int count)
setInputNeurons in interface NeuralNetworkPatterncount - Neuron count.public final void setOutputNeurons(int count)
setOutputNeurons in interface NeuralNetworkPatterncount - Neuron count.
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