Development of associative memories with transformed data

Li, Lina; Pedrycz, Witold; Li, Zhiwu

doi:10.1016/j.asoc.2017.05.035

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…They have been effectively applied for storing and recalling large color images [33]. Li et al increased the storage capacity of fuzzy associative memories using piecewise linear transformations [34]. Sussner and Schuster proposed interval-valued fuzzy morphological associative memories (IV-FMAMs) designed for storing and retrieving interval-valued fuzzy sets [6].…”

Section: Introductionmentioning

confidence: 99%

Max-C and Min-D Projection Auto-Associative Fuzzy Morphological Memories: Theory and an Application for Face Recognition

dos Santos,

Valle

2023

AppliedMath

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Max-C and min-D projection auto-associative fuzzy morphological memories (max-C and min-D PAFMMs) are two-layer feedforward fuzzy morphological neural networks designed to store and retrieve finite fuzzy sets. This paper addresses the main features of these auto-associative memories: unlimited absolute storage capacity, fast retrieval of stored items, few spurious memories, and excellent tolerance to either dilative or erosive noise. Particular attention is given to the so-called Zadeh’ PAFMM, which exhibits the most significant noise tolerance among the max-C and min-D PAFMMs besides performing no floating-point arithmetic operations. Computational experiments reveal that Zadeh’s max-C PFAMM, combined with a noise masking strategy, yields a fast and robust classifier with a strong potential for face recognition tasks.

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Section: Introductionmentioning

confidence: 99%