Dynamic Associative Memories

Hassoun, M.H.

doi:10.1016/b978-0-444-88740-5.50016-5

Cited by 16 publications

(22 citation statements)

References 48 publications

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“…. , x k constitute an orthonormal set [25,26]. Chung and Lee noted that a similar statement, which can be found below, is true for GFAM models.…”

Section: Generalized Fuzzy Associative Memories Of Chung and Leementioning

confidence: 59%

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Fuzzy Associative Memories and Their Relationship to Mathematical Morphology

Sussner

Valle

2008

Handbook of Granular Computing

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“…. , x k constitute an orthonormal set [25,26]. Chung and Lee noted that a similar statement, which can be found below, is true for GFAM models.…”

Section: Generalized Fuzzy Associative Memories Of Chung and Leementioning

confidence: 59%

“…. , k} is called fundamental memory set and each association (x ξ , y ξ ) in this set is called a fundamental memory [25]. We speak of an autoassociative memory when the fundamental memory set is of the form {(x ξ , x ξ ) : ξ = 1, .…”

Section: Associative Memoriesmentioning

confidence: 99%

Fuzzy Associative Memories and Their Relationship to Mathematical Morphology

Sussner

Valle

2008

Handbook of Granular Computing

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“…When one or several ones are changed to zero, it is known as subtractive noise. The combination between additive and subtractive noise is known as combined noise [6], [7]. In this case, true color images (24 bits image depth) of flowers ( Fig.…”

Section: Resultsmentioning

confidence: 99%

A Modification of the Lernmatrix for Real Valued Data Processing

Hernández

Sánchez-Fernández

Sánchez-Pérez

et al. 2012

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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An associative memory is a binary relationship between inputs and outputs, which is stored in an M matrix. In this paper, we propose a modification of the Steinbuch Lernmatrix model in order to process real-valued patterns, avoiding binarization processes and reducing computational burden. The proposed model is used in experiments with noisy environments, where the performance and efficiency of the memory is proven. A comparison between the proposed and the original model shows a good response and efficiency in the classification process of the new Lernmatrix.

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“…For each k integer and positive, the corresponding association will be denoted as: (x k , y k ). Associative memory M is represented by a matrix whose ij-th component is m ij [5]. Memory M is generated from an a priori finite set of known associations, called the fundamental set of associations.…”

Section: Associative Memoriesmentioning

confidence: 99%

Thresholded Learning Matrix for Efficient Pattern Recalling

Aldape-Pérez

Román-Godínez

Camacho-Nieto

2008

Lecture Notes in Computer Science

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The Lernmatrix, which is the first known model of associative memory, is a heteroassociative memory that can easily work as a binary pattern classifier if output patterns are appropriately chosen. However, this mathematical model undergoes fundamental patterns misclassification whenever crossbars saturation occurs. In this paper, a novel algorithm that overcomes Lernmatrix weaknesses is proposed. The crossbars saturation occurrence is solved by means of a dynamic threshold value which is computed for each recalled pattern. The algorithm applies the dynamic threshold value over the ambiguously recalled class vector in order to obtain a sentinel vector which is used for uncertainty elimination purposes. The efficiency and effectiveness of our approach is demonstrated through comparisons with other methods using real-world data.

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Dynamic Associative Memories

Cited by 16 publications

References 48 publications

Fuzzy Associative Memories and Their Relationship to Mathematical Morphology

Fuzzy Associative Memories and Their Relationship to Mathematical Morphology

A Modification of the Lernmatrix for Real Valued Data Processing

Thresholded Learning Matrix for Efficient Pattern Recalling

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