Implementation of fuzzy associative memory toward optimizing a neural network model to predict total iron binding capacity

Kusumadewi, Sri; Rosita, Linda; Gustri Wahyuni, Elyza

doi:10.1016/j.bspc.2023.105297

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2023

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“…Several associative memory models have been introduced in the literature, and their applications range from optimization [3,4] and prediction [5][6][7] to image processing and analysis [8][9][10]. Associative memory models have also been applied for pattern classification [11][12][13][14], including face recognition [15].…”

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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