On the reduction of complexity in the architecture of fuzzy ARTMAP with dynamic decay adjustment

2006 ◽  
Vol 69 (16-18) ◽  
pp. 2456-2460 ◽  
Author(s):  
Shing Chiang Tan ◽  
M.V.C. Rao ◽  
Chee Peng Lim
Keyword(s):  
Fuzzy Artmap ◽  
Dynamic Decay Adjustment

IDENTIFICATION OF COHERENT STRUCTURES IN TURBULENT SHEAR FLOWS WITH A FUZZY ARTMAP NEURAL NETWORK

1996 ◽  
Vol 07 (05) ◽  
pp. 559-568 ◽  
Author(s):  
J. FERRE-GINE ◽  
R. RALLO ◽  
A. ARENAS ◽  
FRANCE GIRALT
Keyword(s):  
Neural Network ◽  
Template Matching ◽  
Phase Relationship ◽  
Fuzzy Artmap ◽  
Turbulent Shear ◽  
Training Procedure ◽  
Full Data ◽  
The Core ◽  
Data Files ◽  
Number Of Classes

An implementation of a Fuzzy Artmap neural network is used to detect and to identify (recognise) structures (patterns) embedded in the velocity field of a turbulent wake behind a circular cylinder. The net is trained to recognise both clockwise and anticlockwise eddies present in the u and v velocity fields at 420 diameters downstream of the cylinder that generates the wake, using a pre-processed part of the recorded velocity data. The phase relationship that exists between the angles of the velocity vectors of an eddy pattern is used to reduce the number of classes contained in the data, before the start of the training procedure. The net was made stricter by increasing the vigilance parameter within the interval [0.90, 0.95] and a set of net-weights were obtained for each value. Full data files were scanned with the net classifying patterns according to their phase characteristics. The net classifies about 27% of the recorded signals as eddy motions, with the strictest vigilance parameter and without the need to impose external initial templates. Spanwise distances (homogeneous direction of the flow) within the centres of the eddies identified suggest that they form pairs of counter-rotating vortices (double rollers). The number of patterns selected with Fuzzy Artmap is lower than that reported for template matching because the net classifies eddies according to the recirculating pattern present at the core or central region, while template matching extends the region over which correlation between data and template is performed. In both cases, the topology of educed patterns is in agreement.

Adaptive pixel unmixing based on a fuzzy ARTMAP neural network with selective endmembers

2015 ◽  
Vol 20 (12) ◽  
pp. 4723-4732 ◽  
Author(s):  
Ke Wu ◽  
Lifei Wei ◽  
Xianmin Wang ◽  
Ruiqing Niu
Keyword(s):  
Neural Network ◽  
Fuzzy Artmap

scholarly journals Fuzzy Lattice Reasoning for Pattern Classification Using a New Positive Valuation Function

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Yazdan Jamshidi Khezeli ◽  
Hossein Nezamabadi-pour
Keyword(s):  
Pattern Classification ◽  
Fuzzy Artmap ◽  
Lattice Data ◽  
Valuation Function ◽  
Measure Function ◽  
Inclusion Measure ◽  
Domain Extension ◽  
Fuzzy Lattice ◽  
Neural Classifier ◽  
Modified Algorithm

This paper describes an enhancement of fuzzy lattice reasoning (FLR) classifier for pattern classification based on a positive valuation function. Fuzzy lattice reasoning (FLR) was described lately as a lattice data domain extension of fuzzy ARTMAP neural classifier based on a lattice inclusion measure function. In this work, we improve the performance of FLR classifier by defining a new nonlinear positive valuation function. As a consequence, the modified algorithm achieves better classification results. The effectiveness of the modified FLR is demonstrated by examples on several well-known pattern recognition benchmarks.

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