org.neuroph.nnet.learning
Class BinaryDeltaRule
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.PerceptronLearning
org.neuroph.nnet.learning.BinaryDeltaRule
- All Implemented Interfaces:
- Serializable
public class BinaryDeltaRule
- extends PerceptronLearning
Delta rule learning algorithm for perceptrons with step functions.
The difference to Perceptronlearning is that Delta Rule calculates error
before the non-lnear step transfer function
- Author:
- Zoran Sevarac
- See Also:
- Serialized Form
Method Summary |
double |
getErrorCorrection()
Gets the errorCorrection parametar |
void |
setErrorCorrection(double errorCorrection)
Sets the errorCorrection parametar |
protected void |
updateNetworkWeights(double[] patternError)
This method implements weight update procedure for the whole network for
this learning rule |
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 |
BinaryDeltaRule
public BinaryDeltaRule()
- Creates new BinaryDeltaRule learning
updateNetworkWeights
protected void updateNetworkWeights(double[] patternError)
- This method implements weight update procedure for the whole network for
this learning rule
- Overrides:
updateNetworkWeights
in class LMS
- Parameters:
patternError
- single pattern error vector
if the output is 0 and required value is 1, increase rthe weights
if the output is 1 and required value is 0, decrease the weights
otherwice leave weights unchanged- See Also:
SupervisedLearning.calculateOutputError(double[], double[])
,
learnPattern
getErrorCorrection
public double getErrorCorrection()
- Gets the errorCorrection parametar
- Returns:
- errorCorrection parametar
setErrorCorrection
public void setErrorCorrection(double errorCorrection)
- Sets the errorCorrection parametar
- Parameters:
errorCorrection
- the value for errorCorrection parametar
Copyright © 2012. All Rights Reserved.