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## 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

Field Summary

Fields inherited from class org.neuroph.core.learning.SupervisedLearning
`maxError, outputError, previousEpochError, totalNetworkError, totalSquaredErrorSum`

Fields inherited from class org.neuroph.core.learning.IterativeLearning
`currentIteration, iterationsLimited, learningRate, maxIterations`

Fields inherited from class org.neuroph.core.learning.LearningRule
`listeners, neuralNetwork`

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.nnet.learning.SigmoidDeltaRule
`calculateErrorAndUpdateOutputNeurons`

Methods inherited from class org.neuroph.nnet.learning.LMS
`updateNeuronWeights`

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 org.neuroph.core.learning.IterativeLearning
`doOneLearningIteration, getCurrentIteration, getLearningRate, isPausedLearning, learn, learn, pause, resume, setLearningRate, setMaxIterations`

Methods inherited from class org.neuroph.core.learning.LearningRule
`addListener, fireLearningEvent, getNeuralNetwork, getTrainingSet, isStopped, removeListener, setNeuralNetwork, setTrainingSet, stopLearning`

Methods inherited from class java.lang.Object
`clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

### BackPropagation

    public BackPropagation()

  Creates new instance of BackPropagation learning

Method Detail

### 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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