Applying Novel Supervised Fuzzy Adaptive Resonance Theory (SF-ART) Neural Network and Biorthogonal Wavelets for Ballistocardiogram Diagnosis

2006 ◽  
Author(s):  
Alireza Akhbardeh ◽  
Sakari Junnila ◽  
Teemu Koivistoinen ◽  
Alpo Varri
Keyword(s):  
Neural Network ◽  
Adaptive Resonance Theory ◽  
Biorthogonal Wavelets ◽  
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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