Towards a high capacity fuzzy associative memory model

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Kosko's fuzzy associative memory (FAM) is the very first example to use neural networks to articulate fuzzy rules for fuzzy systems. Despite its simplicity and modularity, the model suffers from extremely low memory capacity, i.e., single rule pattern storage, and hence it is limited to small rule-base applications. In this paper, a high capacity FAM model called fuzzy relational memory (FRM) is proposed. Based upon the well-developed theoretical results of solving fuzzy relational equations, a theorem for perfect recalls of all stored rules is established and two effective encoding algorithms namely orthogonal encoding and weighted encoding are devised. The performance of the new model is reported and compared with that of the FAM model through numerous examples.
Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE
Pages1595-1599
Number of pages5
Publication statusPublished - 1 Dec 1994
Externally publishedYes
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, United States
Duration: 27 Jun 199429 Jun 1994

Conference

ConferenceProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CountryUnited States
CityOrlando, FL
Period27/06/9429/06/94

ASJC Scopus subject areas

  • Software

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