org.neuroph.nnet.learning

Class QuickPropagation

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.BackPropagation

org.neuroph.nnet.learning.QuickPropagation

All Implemented Interfaces:

java.io.Serializable

public class QuickPropagation
extends BackPropagation

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	QuickPropagation.QuickPropData

Constructor Summary

Constructors

Constructor and Description
QuickPropagation()

Method Summary

Modifier and Type	Method and Description
void	calculateWeightChanges(Neuron neuron) This method calculates weights changes for the single neuron.

Methods inherited from class org.neuroph.core.learning.SupervisedLearning

doLearningEpoch, getErrorFunction, getMaxError, getMinErrorChange, getMinErrorChangeIterationsCount, getMinErrorChangeIterationsLimit, getPreviousEpochError, getTotalNetworkError, isBatchMode, learn, learn, setBatchMode, setErrorFunction, setMaxError, setMinErrorChange, setMinErrorChangeIterationsLimit

Methods inherited from class org.neuroph.core.learning.IterativeLearning

doOneLearningIteration, getCurrentIteration, getLearningRate, getMaxIterations, isIterationsLimited, isPausedLearning, learn, learn, pause, resume, setLearningRate, setMaxIterations

Methods inherited from class org.neuroph.core.learning.LearningRule

addListener, getNeuralNetwork, getTrainingSet, isStopped, removeListener, setNeuralNetwork, setTrainingSet, stopLearning

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

QuickPropagation

public QuickPropagation()

Method Detail

calculateWeightChanges

public void calculateWeightChanges(Neuron neuron)

Description copied from class: LMS

This method calculates weights changes for the single neuron. It iterates through all neuron's input connections, and calculates/set weight change for each weight using formula deltaWeight = -learningRate * delta * input where delta is a neuron error, a difference between desired/target and actual output for specific neuron neuronError = desiredOutput[i] - actualOutput[i] (see method SuprevisedLearning.calculateOutputError)

Parameters:

neuron - neuron to update weights

See Also:

LMS.calculateWeightChanges(double[])