2023
DOI: 10.1109/jiot.2022.3228748
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A New Cross Ring Neural Network: Dynamic Investigations and Application to WBAN

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Cited by 23 publications
(9 citation statements)
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“…On one hand, some brain functions can be well understood through the study of the dynamics of the HNN, for example, memory [6], neuroregulation [7], and so on [8]. On the other hand, the HNN with complex dynamics can be widely applied in the field of artificial intelligence to solve practical issues, such as pattern recognition [9], image processing [10], as well as combinatorial optimization [11]. Over the past few decades, an enormous number of improved HNN models have emerged to generate complex dynamical behaviors such as chaos [12], coexisting behaviors [13], and synchronization [14].…”
Section: Introductionmentioning
confidence: 99%
“…On one hand, some brain functions can be well understood through the study of the dynamics of the HNN, for example, memory [6], neuroregulation [7], and so on [8]. On the other hand, the HNN with complex dynamics can be widely applied in the field of artificial intelligence to solve practical issues, such as pattern recognition [9], image processing [10], as well as combinatorial optimization [11]. Over the past few decades, an enormous number of improved HNN models have emerged to generate complex dynamical behaviors such as chaos [12], coexisting behaviors [13], and synchronization [14].…”
Section: Introductionmentioning
confidence: 99%
“…With the rapid development of the Internet, artificial intelligence technology is more and more closely connected with people's lives, [1][2][3][4][5][6] and different types of data can be rapidly disseminated and diffused through the network, in which the issue of image information security has become more and more serious. [7][8][9] In order to address the above issues, some image encryption schemes have been proposed.…”
Section: Introductionmentioning
confidence: 99%
“…Recently, Jiang et al [2] proposed a cross-ring neural network model based on the Hopfield neural network to reduce bandwidth saturation in communication channels and securing the transferred medical data. Yu et al [3] constructed a 4D memristive Hopfield neural network (MHNN) which can perform complex dynamics and performs well in image encryption applications for the significant complexity of multiscroll.…”
Section: Introductionmentioning
confidence: 99%
“…Since the HNN was proposed, various HNNs have been proposed and their chaotic behaviors studied. For example, normal chaos [ 9 , 10 , 11 ] and hyperchaos [ 12 , 13 , 14 ] have been detected in some small HNNs with several neurons. Especially in recent years, memristive neural networks have received much attention from many scholars [ 15 , 16 ].…”
Section: Introductionmentioning
confidence: 99%