Title

Analog Circuit Design And Implementation Of An Adaptive Resonance Theory (Art) Neural Network Architecture

Abstract

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.

Publication Date

9-2-1993

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

1965

Number of Pages

244-255

Document Type

Article; Proceedings Paper

Identifier

scopus

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.152540

Socpus ID

85075827962 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85075827962

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