Articles listed below give examples of ART-based systems being used outside the CNS department at Boston University.
| Categories |
Topics:
Image Analysis,
Applications:
Character Recognition,
Models:
Fuzzy ARTMAP, |
| Author(s) |
Bortolozzi, F. |
Murshed, N. |
Sabourin, R. |
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| Abstract |
This work proposes a new approach to signature verification. It is inspired by the human learning and the approach adopted by the expert examiner of signatures, in which an a priori knowledge of the class of forgeries is not ... |
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| Categories |
Topics:
Image Analysis,
Applications:
Character Recognition,
Models:
Fuzzy ARTMAP, |
| Author(s) |
Bortolozzi, F. |
Murshed, N. |
Sabourin, R. |
|
| Abstract |
This work presents a fuzzy ARTMAP based off-line signature verification system. Compared to the conventional systems proposed thus far, the presented system is trained with genuine signatures only. Six experiments have been ... |
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| Categories |
Topics:
Neural Hardware,
Models:
ART 1, |
| Author(s) |
Georgiopoulos, M. |
Liou, J.J. |
Wuerz, D. |
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| Abstract |
This paper outlines the design and simulation of an analogue integrated circuit for the adaptive resonanace theory (ART1) neural network. The circuit is designed based on a set of coupled differential equations which ... |
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| Categories |
Topics:
Neural Hardware,
Models:
ART 1,
Modified ART, |
| Author(s) |
Georgiopoulos, M. |
Heileman, G.L. |
Ho, C.S. |
Liou, J.J. |
Christodoulou, C. |
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| Abstract |
An analogue circuit implementation is presented for an adaptive resonance theory neural network architecture, called the augmented ART-1 neural network (AARTI-NN). The AARTI-NN is a modification of the popular ARTI-NN, ... |
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| Categories |
Topics:
Mathematical Foundations of Neural Networks,
Models:
ART 1, |
| Author(s) |
Abdallah, C. |
Georgiopoulos, M. |
Heileman, G.L. |
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| Abstract |
A set of nonlinear differential equations that describe the dynamics of the ART1 model are presented, along with the motivation for their use. These equations are extensions of those developed by Carpenter and Grossberg ... |
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| Categories |
Topics:
Machine Learning,
Applications:
Chemical Analysis,
Models:
ART 2-A, |
| Author(s) |
Hopke, P. |
Xie, Ying |
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| Abstract |
Airborne particle classification that leads to particle source identification is important to both the improvement of the environment and the protection of public health. In this study, individual airborne particles were ... |
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| Categories |
Topics:
Machine Learning,
Models:
ART 1,
ART 2 / Fuzzy ART, |
| Author(s) |
Carpenter, G.A. |
Grossberg, S. |
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| Abstract |
In a pattern recognition system, input signals are applied to a short term feature representation field of nodes. A pattern from the short term feature representation field selects at least one category node in a category ... |
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| Categories |
Topics:
Machine Learning,
Applications:
Remote Sensing,
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| Author(s) |
Dowla, F.U. |
Jarpe, S.P. |
Maurer, W. |
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| Abstract |
In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. ... |
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| Categories |
Topics:
Neural Hardware,
Models:
Modified ART, |
| Author(s) |
Caudell, T.P. |
Wunsch, D.C. |
McGann, R.K. |
Morris, D.J. |
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| Abstract |
We describe a novel adaptive resonance theory (ART) device that is fully optical in the input-output processing path. This device is based on holographic information processing in a photorefractive crystal. This sets up an ... |
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| Categories |
Topics:
Machine Learning,
Applications:
Character Recognition,
Models:
ART 1,
Modified ART, |
| Author(s) |
Srinivasan, N. |
Jouaneh, M. |
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| Abstract |
A novel invariant pattern recognition machine is proposed based on a modified ART architecture. Invariance is achieved by adding a new layer called F23, beyond the F2 layer in the ART architecture. The design of the weight ... |
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