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## Package org.neuroph.nnet.learning

Provides implementations of specific neural network learning algorithms.

See:
Description

Class Summary
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
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.
SigmoidDeltaRule | Delta rule learning algorithm for perceptrons with sigmoid (or any other diferentiable continuous) functions.
SupervisedHebbianLearning | Supervised hebbian learning rule.
UnsupervisedHebbianLearning | Unsupervised hebbian learning rule.

## Package org.neuroph.nnet.learning Description

Provides implementations of specific neural network learning algorithms.

Overview | Package | Class | Use | Tree | Deprecated | Index | Help
| Neuroph
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