Package org.neuroph.nnet.learning

Provides implementations of specific neural network learning algorithms.

See:
          Description

Class Summary
AntiHebbianLearning A variant of Hebbian learning called Anti-Hebbian learning.
BackPropagation Back Propagation learning rule for Multi Layer Perceptron neural networks.
BinaryDeltaRule Delta rule learning algorithm for perceptrons with step functions.
BinaryHebbianLearning Hebbian-like learning algorithm used for Hopfield network.
CompetitiveLearning Competitive learning rule.
DynamicBackPropagation Backpropagation learning rule with dynamic learning rate and momentum
GeneralizedHebbianLearning A variant of Hebbian learning called Generalized Hebbian learning.
HopfieldLearning Learning algorithm for the Hopfield neural network.
InstarLearning Hebbian-like learning rule for Instar network.
KohonenLearning Learning algorithm for Kohonen network.
LMS LMS learning rule for neural networks.
MomentumBackpropagation Backpropagation learning rule with momentum.
OjaLearning Oja learning rule wich is a modification of unsupervised hebbian learning.
OutstarLearning Hebbian-like learning rule for Outstar network.
PerceptronLearning Perceptron learning rule for perceptron neural networks.
ResilientPropagation Resilient Propagation learning rule used for Multi Layer Perceptron neural networks.
SigmoidDeltaRule Delta rule learning algorithm for perceptrons with sigmoid (or any other diferentiable continuous) functions.
SimulatedAnnealingLearning This class implements a simulated annealing learning rule for supervised neural networks.
SupervisedHebbianLearning Supervised hebbian learning rule.
UnsupervisedHebbianLearning Unsupervised hebbian learning rule.
 

Package org.neuroph.nnet.learning Description

Provides implementations of specific neural network learning algorithms.



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