| Name | Description |
|---|---|
| ADALINEPattern | Construct an ADALINE neural network. |
| ART1Pattern | |
| BAMPattern | |
| BoltzmannPattern | |
| CPNPattern | |
| ElmanPattern | |
| FeedForwardPattern | |
| HopfieldPattern | Create a Hopfield pattern. A Hopfield neural network has a single layer that functions both as the input and output layers. There are no hidden layers. Hopfield networks are used for basic pattern recognition. When a Hopfield network recognizes a pattern, it "echos" that pattern on the output. |
| JordanPattern | This class is used to generate an Jordan style recurrent neural network. This network type consists of three regular layers, an input output and hidden layer. There is also a context layer which accepts output from the output layer and outputs back to the hidden layer. This makes it a recurrent neural network. The Jordan neural network is useful for temporal input data. The specified activation function will be used on all layers. The Jordan neural network is similar to the Elman neural network. |
| PatternError | |
| RadialBasisPattern | |
| SOMPattern | |
| SVMPattern |