On new fuzzy morphological associative memories

Author

Wang, S.T. ; Lu, H.J.

Author_Institution

Dept. of Comput. Sci. & Technol., Southern Yangtze Univ., Jiangsu, China

Volume

12

Issue

3

fYear

2004

fDate

6/1/2004 12:00:00 AM

Firstpage

316

Lastpage

323

Abstract

In this paper, the new fuzzy morphological associative memories (FMAMs) based on fuzzy operations (∧,·) and (∨,·) are presented. FMAM with (∨,·) is extremely robust for dilative noise and FMAM with (∧,·) is extremely robust for erosive noise. Autoassociative FMAM has the unlimited storage capability and can converge in one step. The convex autoassociative FMAM can be used to achieve a reasonable tradeoff for the mixed noise. Finally, comparisons between autoassociative FMAM and the famous FAM are discussed. FMAM, as another new encoding way of fuzzy rules, still has a multitude of open problems worthy to explore in the future.

Keywords

content-addressable storage; fuzzy logic; fuzzy neural nets; memory architecture; random noise; dilative noise; fuzzy morphological associative memories; fuzzy operations; fuzzy rules; mixed noise; storage capability; Associative memory; Computer science; Convergence; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Image coding; Neural networks; Noise robustness; Subspace constraints; Associative memories; convergence; fuzzy operations; fuzzy rules; storage capability;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/TFUZZ.2004.825977

Filename

1303602