Analysis and synthesis of a class of discrete-time neural networks described on hypercubes

A. N. Michel, Jennie Si, G. Yen

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

Abstract

The qualitative properties of neural networks described by a system of first-order linear ordinary difference equations which are defined on a closed hypercube of the state space with solutions extended to the boundary of the hypercube are investigated. The class of systems considered can easily be implemented in digital hardware. When implemented by a serial processor (e.g., in digital simulations), the presented class of neural networks offers considerable advantages over digital simulations of the differential equations used to represent the continuous-time neural networks considered in previously published work. The applicability of the present results is demonstrated by means of several specific examples. These include pattern recognition applications.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
PublisherPubl by IEEE
Pages700-703
Number of pages4
Volume1
StatePublished - 1990
Externally publishedYes
Event1990 IEEE International Symposium on Circuits and Systems Part 4 (of 4) - New Orleans, LA, USA
Duration: May 1 1990May 3 1990

Other

Other1990 IEEE International Symposium on Circuits and Systems Part 4 (of 4)
CityNew Orleans, LA, USA
Period5/1/905/3/90

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

Cite this

Michel, A. N., Si, J., & Yen, G. (1990). Analysis and synthesis of a class of discrete-time neural networks described on hypercubes. In Proceedings - IEEE International Symposium on Circuits and Systems (Vol. 1, pp. 700-703). Publ by IEEE.