## Uses of Interface
org.encog.neural.pattern.NeuralNetworkPattern

- Packages that use NeuralNetworkPattern
  Package | Description
  --- | ---
  org.encog.neural.pattern |
  org.encog.neural.prune |

- - ### Uses of NeuralNetworkPattern in org.encog.neural.pattern

    Classes in org.encog.neural.pattern that implement NeuralNetworkPattern
    Modifier and Type | Class and Description
    --- | ---
    `class` | `ADALINEPattern` Construct an ADALINE neural network.
    `class` | `ART1Pattern` Pattern to create an ART-1 neural network.
    `class` | `BAMPattern` Construct a Bidirectional Access Memory (BAM) neural network.
    `class` | `BoltzmannPattern` Pattern to create a Boltzmann machine.
    `class` | `CPNPattern` Pattern that creates a CPN neural network.
    `class` | `ElmanPattern` This class is used to generate an Elman style recurrent neural network.
    `class` | `FeedForwardPattern` Used to create feedforward neural networks.
    `class` | `HopfieldPattern` Create a Hopfield pattern.
    `class` | `JordanPattern` This class is used to generate an Jordan style recurrent neural network.
    `class` | `PNNPattern` Pattern to create a PNN.
    `class` | `RadialBasisPattern` A radial basis function (RBF) network uses several radial basis functions to provide a more dynamic hidden layer activation function than many other types of neural network.
    `class` | `SOMPattern` A self organizing map is a neural network pattern with an input and output layer.
    `class` | `SVMPattern` A pattern to create support vector machines.

  - ### Uses of NeuralNetworkPattern in org.encog.neural.prune

    Methods in org.encog.neural.prune that return NeuralNetworkPattern
    Modifier and Type | Method and Description
    --- | ---
    `NeuralNetworkPattern` | PruneIncremental.`getPattern()`

    Constructors in org.encog.neural.prune with parameters of type NeuralNetworkPattern
    Constructor and Description
    `PruneIncremental(MLDataSet training, NeuralNetworkPattern pattern, int iterations, int weightTries, int numTopResults, StatusReportable report)` Construct an object to determine the optimal number of hidden layers and neurons for the specified training data and pattern.

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