Abstract
In this article we introduce a continuous time implementation of adaptive resonance theory (ART). ART designed by Grossberg concerns neural networks that self-organize stable pattern recognition categories of arbitrary sequences of input patterns. In contrast to the current implementations of ART we introduce a complete implementation of an ART network, including all regulatory and logical functions, as a system of ordinary differential equations capable of stand-alone running in real time. This means that transient behavior is kept in tact. This implementation of ART is based on ART 2 and is called Exact ART. Exact ART includes an implementation of a gated dipole field and an implementation of the orienting sub-system. The most important features of Exact ART, which are the design principles of ART 2, are proven mathematically. Also simulation studies show that Exact ART self-organizes stable recognition codes that agree with the classification behavior of ART 2.
Original language | English (US) |
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Pages (from-to) | 649-669 |
Number of pages | 21 |
Journal | Neural Networks |
Volume | 10 |
Issue number | 4 |
DOIs | |
State | Published - Jun 1997 |
All Science Journal Classification (ASJC) codes
- Cognitive Neuroscience
- Artificial Intelligence