org.neuroph.nnet.learning
Class BackPropagation
java.lang.Object
org.neuroph.core.learning.LearningRule
org.neuroph.core.learning.IterativeLearning
org.neuroph.core.learning.SupervisedLearning
org.neuroph.nnet.learning.LMS
org.neuroph.nnet.learning.SigmoidDeltaRule
org.neuroph.nnet.learning.BackPropagation
- All Implemented Interfaces:
- Serializable
- Direct Known Subclasses:
- MomentumBackpropagation, ResilientPropagation
public class BackPropagation
- extends SigmoidDeltaRule
Back Propagation learning rule for Multi Layer Perceptron neural networks.
- Author:
- Zoran Sevarac
- See Also:
- Serialized Form
Constructor Summary |
BackPropagation()
Creates new instance of BackPropagation learning |
Method Summary |
protected void |
calculateErrorAndUpdateHiddenNeurons()
This method implements weights adjustment for the hidden layers |
protected double |
calculateHiddenNeuronError(Neuron neuron)
Calculates and returns the neuron's error (neuron's delta) for the given neuron param |
protected void |
updateNetworkWeights(double[] outputError)
This method implements weight update procedure for the whole network
for the specified output error vector |
Methods inherited from class org.neuroph.core.learning.SupervisedLearning |
addToSquaredErrorSum, afterEpoch, beforeEpoch, calculateOutputError, doBatchWeightsUpdate, doLearningEpoch, errorChangeStalled, getMaxError, getMinErrorChange, getMinErrorChangeIterationsCount, getMinErrorChangeIterationsLimit, getPreviousEpochError, getTotalNetworkError, hasReachedStopCondition, isInBatchMode, learn, learn, learnPattern, onStart, setBatchMode, setMaxError, setMinErrorChange, setMinErrorChangeIterationsLimit |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
BackPropagation
public BackPropagation()
- Creates new instance of BackPropagation learning
updateNetworkWeights
protected void updateNetworkWeights(double[] outputError)
- This method implements weight update procedure for the whole network
for the specified output error vector
- Overrides:
updateNetworkWeights
in class SigmoidDeltaRule
- Parameters:
outputError
- output error vector- See Also:
SupervisedLearning.calculateOutputError(double[], double[])
,
learnPattern
calculateErrorAndUpdateHiddenNeurons
protected void calculateErrorAndUpdateHiddenNeurons()
- This method implements weights adjustment for the hidden layers
calculateHiddenNeuronError
protected double calculateHiddenNeuronError(Neuron neuron)
- Calculates and returns the neuron's error (neuron's delta) for the given neuron param
- Parameters:
neuron
- neuron to calculate error for
- Returns:
- neuron error (delta) for the specified neuron
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