Choosing Text Description Language Features for Training and Testing Adaptive Resonance Theory: A Case Study for Botanical Classification

2010 ◽  
Author(s):  
Wen-Sen Lee
Keyword(s):  
Adaptive Resonance Theory ◽  
Description Language ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Botanical Classification

Tree-ART2 Learning Model for Spatial Clustering in Second Dimension

2014 ◽  
Vol 543-547 ◽  
pp. 1934-1938
Author(s):  
Ming Xiao
Keyword(s):  
Network Model ◽  
Spatial Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Adaptive Resonance Theory ◽  
Spatial Distance ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Vector Module

For a clustering algorithm in two-dimension spatial data, the Adaptive Resonance Theory exists not only the shortcomings of pattern drift and vector module of information missing, but also difficultly adapts to spatial data clustering which is irregular distribution. A Tree-ART2 network model was proposed based on the above situation. It retains the memory of old model which maintains the constraint of spatial distance by learning and adjusting LTM pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model can reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. It is showed that TART2 network has higher plasticity and adaptability through compared experiments.

WIS-ART: UNSUPERVISED CLUSTERING WITH RAM DISCRIMINATORS

1992 ◽  
Vol 03 (01) ◽  
pp. 57-63 ◽  
Author(s):  
Eamon P. Fulcher
Keyword(s):  
Random Access ◽  
Test Phase ◽  
Adaptive Resonance Theory ◽  
The Self ◽  
Hierarchical Level ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Pattern Clustering ◽  
Adaptive Pattern ◽  
System Operating

WIS-ART merges the self-organising properties of Adaptive Resonance Theory (ART) with the operation of WISARD, an adaptive pattern recognition machine which uses discriminators of conventional Random Access Memories (RAMs). The result is an unsupervised pattern clustering system operating at near real-time that implements the leader algorithm. ART’s clustering is highly dependent upon the value of a “vigilance” parameter, which is set prior to training. However, for WIS-ART hierarchical clustering is performed automatically by the partitioning of discriminators into “multi-vigilance modules”. Thus, clustering may be controlled during the test phase according to the degree of discrimination (hierarchical level) required. Methods for improving the clustering characteristics of WIS-ART whilst still retaining stability are discussed.

scholarly journals Online Anomaly Detection of Distillation Tower System Using Adaptive Resonance Theory

2017 ◽  
Vol 50 (6) ◽  
pp. 430-438 ◽  
Author(s):  
Yoshinari Hori ◽  
Hiroki Yamamoto ◽  
Tomoko Suzuki ◽  
Jun Okitsu ◽  
Tomohiro Nakamura ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Adaptive Resonance Theory ◽  
Distillation Tower ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Online Anomaly Detection ◽  
Tower System
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