Encog.Neural.Networks.Training.Propagation

Class	Description
CalculatePartialDerivative	Class that is used to calculate the partial derivatives for the error for individual layers of a neural network. This calculation must be performed by each of the propagation techniques.
Propagation	Implements basic functionality that is needed by each of the propagation methods. The specifics of each of the propagation methods is implemented inside of the PropagationMethod interface implementors.
PropagationLevel	Holds a level worth of information used by each of the propagation methods. A level is defined as all of the layers that feed a single next layer. In a pure feedforward neural network there will be only one layer per level. However, recurrent neural networks will contain multiple layers per level.
PropagationSynapse	The back propagation training algorithms requires training data to be stored for each of the synapses. The propagation class creates a PropagationSynapse object for each of the synapses in the neural network that it is training.

Interface	Description
IPropagationMethod	Defines the specifics to one of the propagation methods. The individual ways that each of the propagation methods uses to modify the weight and] threshold matrix are defined here.

Encog.Neural.Networks.Training.Propagation Namespace