Analysis and synthesis of discrete-time neural networks with multilevel threshold functions

Jennie Si, A. N. Michel

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

12 Scopus citations

Abstract

In contrast to the usual types of neural networks which utilize two states for each neuron, a class of synchronous discrete-time multilevel threshold neural networks is developed. A qualitative analysis and a synthesis procedure of the class of neural networks constitute the principal contributions of this work. The applicability of the class of neural networks is demonstrated by means of a gray-level image processing example in which each neuron of the present model assumes one of 16 values. In doing so, the number of neurons and the number of interconnections are reduced, when compared to the usual binary state networks.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
PublisherPubl by IEEE
Pages1461-1464
Number of pages4
Volume3
StatePublished - 1991
Externally publishedYes
Event1991 IEEE International Symposium on Circuits and Systems Part 4 (of 5) - Singapore, Singapore
Duration: Jun 11 1991Jun 14 1991

Other

Other1991 IEEE International Symposium on Circuits and Systems Part 4 (of 5)
CitySingapore, Singapore
Period6/11/916/14/91

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Analysis and synthesis of discrete-time neural networks with multilevel threshold functions'. Together they form a unique fingerprint.

Cite this