| Name | Description |
|---|---|
| ADALINEPattern | Construct an adaline neural network. |
| ART1Pattern | Pattern to create an ART-1 neural network. |
| BAMPattern | Construct a Bidirectional Access Memory (BAM) neural network. This neural network type learns to associate one pattern with another. The two patterns do not need to be of the same length. This network has two that are connected to each other. Though they are labeled as input and output layers to Encog, they are both equal, and should simply be thought of as the two layers that make up the net. |
| BoltzmannPattern | Pattern to create a Boltzmann machine. |
| CPNPattern | Pattern that creates a CPN neural network. |
| ElmanPattern | This class is used to generate an Elman 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 hidden layer and outputs back to the hidden layer. This makes it a recurrent neural network. The Elman neural network is useful for temporal input data. The specified activation function will be used on all layers. The Elman neural network is similar to the Jordan neural network. |
| FeedForwardPattern | Used to create feedforward neural networks. A feedforward network has an input and output layers separated by zero or more hidden layers. The feedforward neural network is one of the most common neural network patterns. |
| 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. |
| NEATPattern | Pattern to create a NEAT style neural network. |
| PatternConst | GUI constants for creating the patterns. Specifically default x and y coordinates. |
| PatternError | Indicates an error has occurred in the pattern classes. |
| RSOMPattern | A recurrent self organizing map is a self organizing map that has a recurrent context connection on the hidden layer. This type of neural network is adept at classifying temporal data. |
| 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. It consists of a input, output and hidden layer. |
| SOMPattern | A self organizing map is a neural network pattern with an input and output layer. There is no hidden layer. The winning neuron, which is that neuron with the higest output is the winner, this winning neuron is often used to classify the input into a group. |
