## Uses of Class
org.encog.neural.networks.training.propagation.Propagation

- Packages that use Propagation
  Package | Description
  --- | ---
  org.encog.neural.networks.training.propagation |
  org.encog.neural.networks.training.propagation.back |
  org.encog.neural.networks.training.propagation.manhattan |
  org.encog.neural.networks.training.propagation.quick |
  org.encog.neural.networks.training.propagation.resilient |
  org.encog.neural.networks.training.propagation.scg |

- - ### Uses of Propagation in org.encog.neural.networks.training.propagation

    Constructors in org.encog.neural.networks.training.propagation with parameters of type Propagation
    Constructor and Description
    `GradientWorker(FlatNetwork theNetwork, Propagation theOwner, MLDataSet theTraining, int theLow, int theHigh, double[] flatSpot, ErrorFunction ef)` Construct a gradient worker.

  - ### Uses of Propagation in org.encog.neural.networks.training.propagation.back

    Subclasses of Propagation in org.encog.neural.networks.training.propagation.back
    Modifier and Type | Class and Description
    --- | ---
    `class` | `Backpropagation` This class implements a backpropagation training algorithm for feed forward neural networks.

  - ### Uses of Propagation in org.encog.neural.networks.training.propagation.manhattan

    Subclasses of Propagation in org.encog.neural.networks.training.propagation.manhattan
    Modifier and Type | Class and Description
    --- | ---
    `class` | `ManhattanPropagation` One problem that the backpropagation technique has is that the magnitude of the partial derivative may be calculated too large or too small.

  - ### Uses of Propagation in org.encog.neural.networks.training.propagation.quick

    Subclasses of Propagation in org.encog.neural.networks.training.propagation.quick
    Modifier and Type | Class and Description
    --- | ---
    `class` | `QuickPropagation` QPROP is an efficient training method that is based on Newton's Method.

  - ### Uses of Propagation in org.encog.neural.networks.training.propagation.resilient

    Subclasses of Propagation in org.encog.neural.networks.training.propagation.resilient
    Modifier and Type | Class and Description
    --- | ---
    `class` | `ResilientPropagation` One problem with the backpropagation algorithm is that the magnitude of the partial derivative is usually too large or too small.

  - ### Uses of Propagation in org.encog.neural.networks.training.propagation.scg

    Subclasses of Propagation in org.encog.neural.networks.training.propagation.scg
    Modifier and Type | Class and Description
    --- | ---
    `class` | `ScaledConjugateGradient` This is a training class that makes use of scaled conjugate gradient methods.

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