Encog.Neural.Networks

Class	Description
BasicNetwork	This class implements a neural network. This class works in conjunction the Layer classes. Layers are added to the BasicNetwork to specify the structure of the neural network. The first layer added is the input layer, the layer added is the output layer. Any layers added between these two layers are the hidden layers. The network structure is stored in the structure member. It is important to call: network.getStructure().FinalizeStructure(); Once the neural network has been completely constructed.
NetworkCODEC	This class will extract the "long term memory" of a neural network, that is the weights and threshold values into an array. This array can be used to view the neural network as a linear array of doubles. These values can then be modified and copied back into the neural network. This is very useful for simulated annealing, as well as genetic algorithms.
NeuralOutputHolder	Holds the output from each layer of the neural network. This is very useful for the propagation algorithms that need to examine the output of each individual layer.
NeuralStructure	Holds "cached" information about the structure of the neural network. This is a very good performance boost since the neural network does not need to traverse itself each time a complete collection of layers or synapses is needed.

Interface	Description
INetwork	Interface that defines a neural network.

Encog.Neural.Networks Namespace