- All Implemented Interfaces:
- Serializable, MLClassification, MLInput, MLInputOutput, MLMethod, MLOutput, MLProperties, MLResettable
public class ART1extends ARTimplements MLResettable, MLClassificationImplements an ART1 neural network. An ART1 neural network is trained to recognize bipolar patterns as it is presented data. There is no distinct learning phase, like there is with other neural network types. The ART1 neural network is a type of Adaptive Resonance Theory (ART) neural network. ART1 was developed by Stephen Grossberg and Gail Carpenter. This neural network type supports only bipolar input. The ART1 neural network is trained as it is used. New patterns are presented to the ART1 network, and they are classified into either new, or existing, classes. Once the maximum number of classes have been used the network will report that it is out of classes. ART1 neural networks are used for classification. There are essentially 2 layers in an ART1 network. The first, named the F1 layer, acts as the input. The F1 layer receives bipolar patterns that the network is to classify. The F2 layer specifies the maximum number classes that the ART1 network can recognize. Plasticity is an important part for all Adaptive Resonance Theory (ART) neural networks. Unlike most neural networks, ART1 does not have a distinct training and usage stage. The ART1 network will learn as it is used.
- See Also:
- Serialized Form