A fuzzy adaptive resonance theory—supervised predictive mapping neural network applied to the classification of multivariate chemical data

1998 ◽  
Vol 41 (2) ◽  
pp. 161-170 ◽  
Author(s):  
Xin-Hua Song ◽  
Philip K Hopke ◽  
MaryAnn Bruns ◽  
Deborah A Bossio ◽  
Kate M Scow
Keyword(s):  
Neural Network ◽  
Adaptive Resonance Theory ◽  
Chemical Data ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Predictive Mapping ◽  
Fuzzy Adaptive

Application of Adaptive Resonance Theory Neural Network for MR Brain Tumor Image Classification

2012 ◽  
pp. 154-167 ◽  
Author(s):  
D. Jude Hemanth ◽  
D. Selvathi ◽  
J. Anitha
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Classification Accuracy ◽  
Adaptive Resonance Theory ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Statistical Classifiers ◽  
Mr Brain ◽  
Abnormal Brain

In the present study, the effectiveness of the adaptive resonance theory neural network (ART2) is illustrated in the context of automatic classification of abnormal brain tumor images. Abnormal images from four different classes namely metastase, meningioma, glioma and astrocytoma have been used in this work. Initially, textural features are extracted from these images. An extensive feature selection is performed to optimize the number of features. These optimized features are then used to classify the images using ART2 neural network. Experimental results show promising results for the ART2 network in terms of classification accuracy and convergence rate. A comparison is made with other conventional classifiers to show the superior nature of ART2 neural network. The classification accuracy of the ART2 classifier is significantly higher than the statistical classifiers. ART2 classifier is also computationally feasible over other neural classifiers. Thus this work suggests ART2 neural network as an optimal image classifier which finds application in clinical field.

scholarly journals Classification of Groundwater by Applying Simplified Fuzzy Adaptive Resonance Theory

2021 ◽  
Vol 16 (2) ◽  
pp. 167-176
Author(s):  
Relangi Naga Durga Satya Siva Kiran ◽  
Chaparala Aparna ◽  
Sajja Radhika
Keyword(s):  
Adaptive Resonance Theory ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Fuzzy Adaptive

scholarly journals Hardware Implementation of Parallelized Fuzzy Adaptive Resonance Theory Neural Network

2020 ◽  
pp. 1-11
Author(s):  
Pavlo Tymoshchuk ◽  
s. Shatny
Keyword(s):  
Neural Network ◽  
Discrete Time ◽  
Continuous Time ◽  
Hardware Implementation ◽  
Adaptive Resonance Theory ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Winner Take All ◽  
Fuzzy Adaptive ◽  
Take All

A hardware implementation design of parallelized fuzzy Adaptive Resonance Theory neural network is described and simulated. Parallel category choice and resonance are implemented in the network. Continuous-time and discrete-time winner-take-all neural circuits identifying the largest of M inputs are used as the winner-take-all units. The continuous-time circuit is described by a state equation with a discontinuous right-hand side. The discrete-time counterpart is governed by a difference equation. Corresponding functional block-diagrams of the circuits include M feed-forward hard- limiting neurons and one feedback neuron, which is used to compute the dynamic shift of inputs. The circuits combine arbitrary finite resolution of inputs, high convergence speed to the winner-take-all operation, low computational and hardware implementation complexity, and independence of initial conditions. The circuits are also used for finding elements of input vector with minimal/maximal values to normalize them in the range [0,1].

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