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- Gail A. Carpenter, Stephen Grossberg, Natalya Markuzon, John H. Reynolds, David B. Rosen
- IEEE Trans. Neural Networks
- 1992

A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory… (More)

- Gail A. Carpenter, Stephen Grossberg
- Computer Vision, Graphics, and Image Processing
- 1987

A neural network architecture for the learning of recognition categories is derived. Real-time network dynamics are completely characterized through mathematical analysis and computer simulations. The architecture self-organizes and self-stabilizes its recognition codes in response to arbitrary orderings of arbitrarily many and arbitrarily complex binary… (More)

- Gail A. Carpenter, Stephen Grossberg, David B. Rosen
- Neural Networks
- 1991

-A Fuzzy Adaptive Resonance Theory (ART) model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to… (More)

- Gail A. Carpenter, Stephen Grossberg
- Applied optics
- 1987

Adaptive resonance architectures are neural networks that self-organize stable pattern recognition codes in real-time in response to arbitrary sequences of input patterns. This article introduces ART 2, a class of adaptive resonance architectures which rapidly self-organize pattern recognition categories in response to arbitrary sequences of either analog… (More)

- Gail A. Carpenter, Stephen Grossberg, John H. Reynolds
- Neural Networks
- 1991

-This article introduces a new neural network architecture, called A R T M A P , that autonomously learns to class(~v arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is" built up from a pair of Adaptive Resonance Theory modules (ART, and ARTh) that are capable o f… (More)

- Gail A. Carpenter, Stephen Grossberg
- Computer
- 1988

The adaptive resonance theory (ART) suggests a solution to the stability-plasticity dilemma facing designers of learning systems, namely how to design a learning system that will remain plastic, or adaptive, in response to significant events and yet remain stable in response to irrelevant events. ART architectures are discussed that are neural networks that… (More)

- Gail A. Carpenter, Stephen Grossberg, David B. Rosen
- Neural Networks
- 1991

This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and… (More)

- Gail A. Carpenter, Stephen Grossberg
- Encyclopedia of Machine Learning and Data Mining
- 2010

Permission to copy without fcc all or part of this material is granted provided that: 1. The copies arc not made or distributed for direct commercial advantage; 2. the report title, author, document number, <:md release date appear, and notice is given that copying is by permission of the BOSTON UNIVERSITY CENTER FOR ADAPTIVE SYSTEMS AND DEPARTMENT OF… (More)

- Gail A. Carpenter
- Neural Networks
- 1997

- Gail A. Carpenter, Natalya Markuzon
- Neural Networks
- 1998

For complex database prediction problems such as medical diagnosis, the ARTMAP-IC neural network adds distributed prediction and category instance counting to the basic fuzzy ARTMAP system. For the ARTMAP match tracking algorithm, which controls search following a predictive error, a new version facilitates prediction with sparse or inconsistent data.… (More)