ANALOG CIRCUIT DESIGN BASED ON NEURAL NETWORKS.

Yong B. Cho, Yoshiyasu Takefuji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Neural networks have been studied for many years with the hope of achieving human-like performance in such fields as speech and image recognition. A recent resurgence has resulted from VLSI advances, neural network models, and learning algorithms. Neural networks are also very suitable in certain areas such as NP-complete constraint and satisfaction problems, due to the nature of parallel and distributed processing. Neural network models are composed of a mass of fairly simple computational elements and rich interconnections between the elements. Neural networks operate in a parallel and distributed fashion which may resemble biological neural networks. Behaviors of neurons and the strengths of synaptic interconnections are simulated by operational amplifiers and resistors respectively. Several examples are presented, including how to build neural network components based on analog circuits for simulating neural networks and conventional logic circuits.

Original languageEnglish
Title of host publicationProceedings of the Annual Southeastern Symposium on System Theory
PublisherIEEE
Pages100-105
Number of pages6
ISBN (Print)0818608471
Publication statusPublished - 1988 Jan 1
Externally publishedYes

Publication series

NameProceedings of the Annual Southeastern Symposium on System Theory

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mathematics(all)

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  • Cite this

    Cho, Y. B., & Takefuji, Y. (1988). ANALOG CIRCUIT DESIGN BASED ON NEURAL NETWORKS. In Proceedings of the Annual Southeastern Symposium on System Theory (pp. 100-105). (Proceedings of the Annual Southeastern Symposium on System Theory). IEEE.