Class Summary |
ConnectedNode |
The abstract ConnectedNode class contains the basis for defining a node that is part of
a Network module. |
FeedForwardNeuralNetwork |
This class implement a simple feed-forward neural network (no special properties) |
FeedForwardNeuralNetworkForBackPropLearning |
This class implement a feed-forward neural network with learning capabilitie through
back-propagation. |
ModularNode |
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Module |
The abstract Module class provides the minimal setting to define modular node, i.e. |
Network |
The Network class provides the minimal basis to define a network module. |
NeuralNetwork |
The NeuralNetwork class provides the minimal basis to define a neural network module. |
Neuron |
The neuron class contains every thing needed to embed a functional neuron into a NeuralNetwork
module. |
NeuronalNode |
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NeuronForBackPropLearning |
The NeuronForBackPropLearning class extends a Neuron with methods that makes it possible
to embed such neurons into NeuralNetworks with Back-propagation learning ability (e.g. |
Node |
General Node class from which is derived every elements from atomic node
(e.g. |
RecurrentNeuralNetwork |
This class implements the basis for a recurrent neural network (no special ability). |
RecurrentNeuron |
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SelfOrganizingMap |
This class implement a simple 2D "Kohonen" Self Organizing Map. |
SOMoutputNeuron |
!n : [major optimisation issue] this class may be suppressed and re-implemented as arrays in the SelfOrganizingMap class |
ValueContainer |
a ValueContainer object contains a simple double value and may be linked from and to other
objects. |