Hardware implementation of an adaptive resonance theory (ART) neural network using compensated operational amplifiers

1994 ◽  
Author(s):  
Ching S. Ho ◽  
Juin J. Liou ◽  
Michael Georgiopoulos ◽  
Christos G. Christodoulou
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Adaptive Resonance Theory ◽  
Operational Amplifiers ◽  
Resonance Theory ◽  
Adaptive Resonance

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].

Sign in / Sign up

Export Citation Format

Share Document