Analog Circuit-Design And Implementation Of An Adaptive Resonance Theory (Art) Neural-Network Architecture
Abbreviated Journal Title
Int. J. Electron.
PATTERN-RECOGNITION; Engineering, Electrical & Electronic
An analogue circuit implementation is presented for an adaptive resonance theory neural network architecture, called the augmented ART-1 neural network (AARTI-NN). The AARTI-NN is a modification of the popular ARTI-NN, developed by Carpenter and Grossberg, and it exhibits the same behaviour as the ART1-NN. The AART1-NN is a real-time model, and has the ability to classify an arbitrary set of binary input patterns into different clusters. The design of the AARTI-NN circuit is based on a set of coupled nonlinear differential equations that constitute the AARTI-NN model. The circuit is implemented by utilizing analogue electronic components such as operational amplifiers, transistors, capacitors, and resistors. The implemented circuit is verified using the PSpice circuit simulator, running on Sun workstations. Results obtained from the PSpice circuit simulation compare favourably with simulation results produced by solving the differential equations numerically. The prototype system developed here can be used as a building block for larger AARTI-NN architectures, as well as for other types of ART architectures that involve the AARTI-NN model.
International Journal of Electronics
"Analog Circuit-Design And Implementation Of An Adaptive Resonance Theory (Art) Neural-Network Architecture" (1994). Faculty Bibliography 1990s. 2949.