Analog circuit design and implementation of an adaptive resonance theory (ART) neural network architecture

Ching S. Ho, Juin J. Liou, Michael Georgiopoulos, Gregory L. Heileman, Christos Christodoulou

Research output: Contribution to journalConference articlepeer-review


This paper presents an analog circuit implementation for an adaptive resonance theory neural network architecture, called the augmented ART-1 neural network (AART1-NN). The AART1-NN is a modification of the popular ART1-NN, developed by Carpenter and Grossberg, and it exhibits the same behavior 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 AART1-NN circuit is based on a set of coupled nonlinear differential equations that constitute the AART1-NN model. The circuit is implemented by utilizing analog 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 favorably with simulation results produced by solving the differential equations numerically. The prototype system developed here can be used as a building block for larger AART1-NN architectures, as well as for other types of ART architectures that involve the AART1-NN model.

Original languageEnglish (US)
Pages (from-to)244-255
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Sep 2 1993
Externally publishedYes
EventApplications of Artificial Neural Networks IV 1993 - Orlando, United States
Duration: Apr 11 1993Apr 16 1993

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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