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Page 5651 of Psychological Abstracts Vol. 80, Issue 12
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1993
Psychological Abstracts
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[In: (PA Vol 80:47584) Pattern recognition by self-organizing neural networks. ...
Hartline [PA Vol 80:47602]
[describes] a neural network architecture for the learning of recognition categories © the architecture self-organizes and self-stabilizes its recognition codes in response to ...
Review of Pattern Recognition by Self-Organizing Neural Networks
1995
Contemporary Psychology
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Pattern Recognition by Self-Organizing
1116
Neural Networks. Cambridge, MA: MIT Press, 1991. 691 pp. ...
ART networks rep- resent one of the few examples of autono- mous learning capability and real-time self- organizing stable pattern recognition in re- sponse to arbitrary sequences of input pat- terns. ...
doi:10.1037/004153
fatcat:bv7odg7r7zgt5irshjzrdld444
Page 2635 of Psychological Abstracts Vol. 79, Issue 6
[page]
1992
Psychological Abstracts
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The focus is on the development of a self-organizing neural network architecture for invariant pattern recognition in a cluttered environment. ...
ARTI self-organizes and self-stabilizes its recognition codes in response to arbitrary orderings of many and complex binary input patterns. ...
Distributed Prediction and Hierarchical Knowledge Discovery by ARTMAP Neural Networks
[chapter]
2003
Lecture Notes in Computer Science
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Adaptive Resonance Theory (ART) neural networks model real-time prediction, search, learning, and recognition. ...
A recent report on industrial uses of neural networks [8] states: "[The] Boeing ... ...
Together with Stephen Grossberg and their students and colleagues, Professor Carpenter has developed the Adaptive Resonance Theory (ART) family of neural networks for fast learning, pattern recognition ...
doi:10.1007/978-3-540-45224-9_1
fatcat:fxxuxkjnhjbh5bduri2fd3c6tu
Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network
2015
Strojniski vestnik
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In this paper, a new approach using discrete wavelet transform and an adaptive resonance theory neural network for crack fault detection of a gearbox is proposed. ...
An adaptive resonance theory neural network is proposed in order to recognize the changing trend of crack faults without known samples on the basis of extracting the relative wavelet energy as an input ...
ACKNOWLEDGEMENTS The research presented in this paper was supported by National Natural Science Foundation of China (No. 51305135). ...
doi:10.5545/sv-jme.2014.1769
fatcat:zg4fhrjexnfjjbtltqzxbwaglu
Working Memory Networks for Learning Temporal Order with Application to Three-Dimensional Visual Object Recognition
1992
Neural Computation
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Pattern Recognition by Self-Organ- izing Neural Networks. The MIT Press, Cambridge, MA.
Cohen, M. A., Grossberg, S., and Stork, D. 1987. ...
and Grossberg 1987).
6 Concluding Remarks
The present model illustrates how a hierarchically organized neural ar- chitecture can self-organize a higher order type of invariant recognition by cascading ...
doi:10.1162/neco.1992.4.2.270
fatcat:xmahaswf5nbyrl7hzltwbclyai
Self-Generation ART-1 Neural Network with Gradient-Descent Method Aid for Latin Alphabet Recognition
2008
Journal of Computer Science
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Problem statement: In this study a self-generation ART-1 neural network that is an efficient algorithm that emulates the self-organizing pattern recognition developed to avoid the stability-plasticity ...
Results: In the simulation test our system can self organize in real time producing stable recognition while getting inputs pattern beyond those originally stored. ...
In this paper, a self-generation of ART-1 neural network with gradient-descent method aid for Latin alphabet recognition is presented. ...
doi:10.3844/jcssp.2008.631.637
fatcat:ty4gxgb3r5fktpqezdomfmbozq
Sign language recognition using competitive learning in the HAVNET neural network
2000
Applications of Artificial Neural Networks in Image Processing V
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It uses an adaptation of the Hausdorff distance to determine the similarity between an input pattern and a learned representation. ...
The system uses the HAusdorf-Voronoi NETwork (HAVNET), an artificial neural network designed for two-dimensional binary pattern recognition. ...
The SOM model is by far the simplest approach to unsupervised learning and self organization in neural networks. ...
doi:10.1117/12.382901
fatcat:joi2qt2psrb57c2inaxcp4nvw4
Page 1225 of Linguistics and Language Behavior Abstracts: LLBA Vol. 28, Issue 3
[page]
1994
Linguistics and Language Behavior Abstracts: LLBA
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. 1 Adaptive-resonance-theory (ART) architectures comprise a group of neural networks that efficiently self-organize stable recognition codes in response to arbitrary sequences of input patterns. ...
The PNN was trained by a self-organizing clustering algo- rithm, a stochastic approximation to the expectation maximization algo- rithm. ...
Self-generation ART Neural Network for Character Recognition
[chapter]
2006
Lecture Notes in Computer Science
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In this paper, we present a novel self-generation, supervised character recognition algorithm based on adaptive resonance theory (ART) artificial neural network (ANN) and delta-bar-delta method. ...
The proposed method can extend itself based on new information contained in input patterns that require nodes of hidden layers in neural networks and effectively find characters. ...
Acknowledgement This research was supported by Korean Ministry of Science and Technology under the National Research Laboratory Project, Korean Ministry of Information and Communication under HNRC-ITRC ...
doi:10.1007/11760023_41
fatcat:ksngqhnx4ra5ndjg36c6s3lebm
Page 2158 of Psychological Abstracts Vol. 77, Issue 9
[page]
1990
Psychological Abstracts
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Neural Networks, 1989, Vol 23), 169-181. —Outlines a neural network architec- ture that self-organizes invariant pattern recognition codes of “noise” images. ...
The processing stages are figure-ground separa- tion, boundary segmentation, invariant filtering, and self-organi- zation of a pattern recognition code by an ART 2 network. ...
ART Neural Networks for Medical Data Analysis and Fast Distributed Learning
[chapter]
2000
Artificial Neural Networks in Medicine and Biology
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ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. ...
The talk will also consider new neural network architectures, including distributed ART (dART), a real-time model of parallel distributed pattem learning that pennits fast as well as slow adaptation, without ...
Acknowledgements: This research was supported in part by the Office of Naval Research (ONR N00014-95-1-0409 and ONR N00014-95-1-0657). ...
doi:10.1007/978-1-4471-0513-8_2
dblp:conf/annimab/CarpenterM00
fatcat:2mx25gfjcve63kgealmhwtl6ha
Page 285 of Neural Computation Vol. 4, Issue 2
[page]
1992
Neural Computation
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A., and Grossberg, S. (eds.) 1991. Pattern Recognition by Self-Organ- izing Neural Networks. The MIT Press, Cambridge, MA.
Cohen, M. A., Grossberg, S., and Stork, D. 1987. ...
Working Memory Networks 285
Carpenter, G. A., and Grossberg, S. 1987. ART 2: Self-organization of stable category recognition codes for analog input patterns. Appl. Opt. 26, 4919- 4930.
Carpenter, G. ...
Page 941 of Psychological Abstracts Vol. 81, Issue 2
[page]
1994
Psychological Abstracts
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—Studies a self- organizing neural tree (a competitive neural network in which the nodes are organized as a tree structure). ...
—Proposes a learning style where significant relations in the input pattern are recognized and expressed by the unsupervised self-organization of dynamic links. ...
Speech Classification Based on Fuzzy Adaptive Resonance Theory
2006
Proceedings of the 9th Joint Conference on Information Sciences (JCIS)
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ART describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes. ...
We utilize fuzzy adaptive resonance theory (FART) to cluster each frame. FART was an extension to ART, performs clustering of its inputs via unsupervised learning. ...
Adaptive resonance theory (ART) describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes [9, 10] . ...
doi:10.2991/jcis.2006.297
dblp:conf/jcis/HsuH06a
fatcat:4fiiewp7areobhtw56hntimgw4
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