Some Aspects of Associative Memory Construction Based on a Hopfield Network

Karpov, Yury L.; Karpov, Leonid E.; Smetanin, Yu. G.

doi:10.1134/s0361768820050023

Cited by 4 publications

(11 citation statements)

References 8 publications

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“…. , p}, is the input pattern to be recalled and V is the matrix obtained in (14). As a result, a column vector of dimension m is generated, whose ith-component can be obtained according to expression (15).…”

Section: αβ Associative Memoriesmentioning

confidence: 99%

“…This fact has inspired some researchers, in the area of pattern recognition, to come up with some models that simulate the behavior of the brain. One of those models is associative memories [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. In the development of associative memory algorithms, one pushes towards those algorithms with greater learning capacity, better performance, and above all, they must be robust to different types of noise [13,[16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Associative memories differ in the form of algebra they use; for instance, min and maxmemories, as their names imply, work in the so-called minimax algebra, that is, minimal and maximal algebra [16,17,19,21,34,35,45], whereas other types of memories work in real algebra [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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New Model of Heteroasociative Min Memory Robust to Acquisition Noise

et al. 2022

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Associative memories in min and max algebra are of great interest for pattern recognition. One property of these is that they are one-shot, that is, in an attempt they converge to the solution without having to iterate. These memories have proven to be very efficient, but they manifest some weakness with mixed noise. If an appropriate kernel is not used, that is, a subset of the pattern to be recalled that is not affected by noise, memories fail noticeably. A possible problem for building kernels with sufficient conditions, using binary and gray-scale images, is not knowing how the noise is registered in these images. A solution to this problem is presented by analyzing the behavior of the acquisition noise. What is new about this analysis is that, noise can be mapped to a distance obtained by a distance transform. Furthermore, this analysis provides the basis for a new model of min heteroassociative memory that is robust to the acquisition/mixed noise. The proposed model is novel because min associative memories are typically inoperative to mixed noise. The new model of heteroassocitative memory obtains very interesting results with this type of noise.

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Section: αβ Associative Memoriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New Model of Heteroasociative Min Memory Robust to Acquisition Noise

et al. 2022

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“…These networks provide a valuable framework for exploring human memory mechanisms, with both continuous and discrete models being extensively studied [2][3][4]. Their practicality lies in their ability to capture associative memory processes, making them indispensable tools for investigating cognition and learning [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Multistable Memristor Synapse-Based Coupled Bi-Hopfield Neuron Model: Dynamic Analysis, Microcontroller Implementation and Image Encryption

Tamba,

Biamou,

Pham

et al. 2024

Electronics

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The memristor, a revolutionary electronic component, mimics both neural synapses and electromagnetic induction phenomena. Recent study challenges are the development of effective neural models and discovering their dynamics. In this study, we propose a novel Hopfield neural network model leveraging multistable memristors, showcasing its efficacy in encoding biomedical images. We investigate the equilibrium states and dynamic behaviors of our designed model through comprehensive numerical simulations, revealing a rich array of phenomena including periodic orbits, chaotic dynamics, and homogeneous coexisting attractors. The practical realization of our model is achieved using a microcontroller, with experimental results demonstrating strong agreement with theoretical analyses. Furthermore, harnessing the chaos inherent in the neural network, we develop a robust biomedical image encryption technique, validated through rigorous computational performance tests.

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“…As a result of their outstanding performance, associative memories have found numerous applications across various fields [7,[9][10][11][12][13][14][15][16][17][18][19], including medicine [9,10,20] and robotics [1,[21][22][23]. Associative memories can be categorized according to their design into two main types: those that are based on the algebra of the reals [24][25][26][27][28][29][30][31][32][33][34][35][36] and those that are based on the minmax algebra [2][3][4][5][6][7]16,18,37,38]. In this paper, we will focus specifically on memories in minmax algebra.…”

Section: Introductionmentioning

confidence: 99%

Generic Model of Max Heteroassociative Memory Robust to Acquisition Noise

et al. 2023

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Associative memories are a significant topic in pattern recognition, and therefore, throughout history, numerous memory models have been designed due to their usefulness. One such model is the associative memory minmax, which is highly efficient at learning and recalling patterns as well as being tolerant of high levels of additive and subtractive noise. However, it is not efficient when it comes to mixed noise. To solve this issue in the associative memory minmax, we present the generic model of heteroassociative memory max robust to acquisition noise (mixed noise). This solution is based on understanding the behavior of acquisition noise and mapping the location of noise in binary images and gray-scale through a distance transform. By controlling the location of the noise, the associative memories minmax become highly efficient. Furthermore, our proposed model allows patterns to contain mixed noise while still being able to recall the learned patterns completely. Our results show that the proposed model outperforms a model that has already solved this type of problem and has proven to overcome existing methods that show some solution to mixed noise. Additionally, we demonstrate that our model is applicable to all associative minmax memories with excellent results.

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Some Aspects of Associative Memory Construction Based on a Hopfield Network

Cited by 4 publications

References 8 publications

New Model of Heteroasociative Min Memory Robust to Acquisition Noise

New Model of Heteroasociative Min Memory Robust to Acquisition Noise

Multistable Memristor Synapse-Based Coupled Bi-Hopfield Neuron Model: Dynamic Analysis, Microcontroller Implementation and Image Encryption

Generic Model of Max Heteroassociative Memory Robust to Acquisition Noise

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