Compare Effective Fuzzy Associative Memories for Grey-Scale Image Recognition

Bình, Phạm Việt; Hoa, Nong Thi

doi:10.1007/978-3-642-36642-0_26

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2018

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“…Many applications of associative memories, however, require storage of vectors with real components or fuzzy sets [1,2,7,6,19,29,32]. The generalized recurrent exponential fuzzy associative memories [28,29] (GRE-FAMs) are designed for the storage and recall of a finite family of fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

Generalized Exponential Bidirectional Fuzzy Associative Memory with Fuzzy Cardinality-Based Similarity Measures Applied to Face Recognition

Souza

Valle

2018

Tend. Mat. Apl. Comput.

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Associative memories are biologically inspired models designed for the storage and recall by association. Such models aim to store a finite set of associations, called the fundamental memory set. The generalized exponential bidirectional fuzzy associative memory (GEB-FAM) is a heteroassociative memory model designed for the storage and recall of fuzzy sets. A similarity measure, that is, a function that indicates how much two fuzzy sets are equal, is at the core of a GEB-FAM model. In this paper, we present a detailed study on the use of cardinality-based similarity measures in the definition of a GEB-FAM. Moreover, we evaluate the performance of the GEB-FAMs defined using such measures in a face recognition problem.

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