Complex Dynamics, Hardware Implementation and Image Encryption Application of Multiscroll Memeristive Hopfield Neural Network With a Novel Local Active Memeristor

Yu, Fei; Kong, Xinxin; Mokbel, Abdulmajeed Abdullah Mohammed; Yao, Wei; Cai, Shuo

doi:10.1109/tcsii.2022.3218468

Cited by 62 publications

(35 citation statements)

References 12 publications

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“…In the machine learning and neuromorphic hardware, the memristor has been applied for proving the effectiveness for edge detection [16]. In the privacy protection of medical data [26], image encryption [27], and audio encryption application [28], multi-scroll memristive Hopfield neural networks have played an important role. In 2011, D Biolek et al presented is a proof that the 'non-crossing-type pinched hysteretic loops' phenomenon cannot occur in ideal memory elements, which are defined axiomatically via corresponding constitutive relations or via other equivalent characteristics and pointed that the 'crossing-type hysteretic loop' is one of their typical fingerprints [29,30].…”

Section: Open Accessmentioning

confidence: 99%

AC power analysis for second-order memory elements

Liu

Liu²,

Luo³

et al. 2023

Front. Phys.

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As the product of a circuit’s voltage and current, apparent power (S) is of paramount necessity and importance in electrical utilities, electronics, communication, and neural network systems. Based on the existing AC power analysis on the two-terminal passive elements (i.e., R, L, and C), some in-depth research on AC apparent power calculations for second-order memory elements and memristive systems is introduced to help with revealing their complex and unique non-linear phenomena. This paper derives the forms of real power, reactive power, and apparent power for the proposed second-order memory elements (i.e., MR, MC, and ML) and reveals the difference between ideal memory elements and traditional passive ones (i.e., R, C, and L). For all involved memory elements, harmonic values and an extra term occur in the expression of powers to represent their memory characteristics. Especially, the real power is a function of a circuit’s dissipative elements (usually resistances R), but not exactly the memristor (MR). Then, the corresponding curves could be depicted, which demonstrate the differences between R/C/L and MR/MC/ML and verified that harmonic values existed in SMR/SMC/SML, meaning that it would perpetually supply energy when operated with an alternating current.

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Section: Open Accessmentioning

confidence: 99%

AC power analysis for second-order memory elements

Liu

Liu²,

Luo³

et al. 2023

Front. Phys.

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“…In 1971, Chua first put forward the concept of a memristor as a device to describe the relationship between the charge and the flux [26]. Since HP Laboratory developed the entity of memristor in 2008 [27], continuous memristors have been widely used in 2 of 18 neural networks [28][29][30][31][32][33][34] and neural morphological circuits [35][36][37][38][39]. Ding et al [40] studied the hidden coexisting firing patterns of two heterogeneous fractional-order HR neurons coupled with a memristor and applied them to image encryption.…”

Section: Introductionmentioning

confidence: 99%

Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

et al. 2023

Fractal Fract

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In order to enrich the dynamic behaviors of discrete neuron models and more effectively mimic biological neural networks, this paper proposes a bistable locally active discrete memristor (LADM) model to mimic synapses. We explored the dynamic behaviors of neural networks by introducing the LADM into two identical Rulkov neurons. Based on numerical simulation, the neural network manifested multistability and new firing behaviors under different system parameters and initial values. In addition, the phase synchronization between the neurons was explored. Additionally, it is worth mentioning that the Rulkov neurons showed synchronization transition behavior; that is, anti-phase synchronization changed to in-phase synchronization with the change in the coupling strength. In particular, the anti-phase synchronization of different firing patterns in the neural network was investigated. This can characterize the different firing behaviors of coupled homogeneous neurons in the different functional areas of the brain, which is helpful to understand the formation of functional areas. This paper has a potential research value and lays the foundation for biological neuron experiments and neuron-based engineering applications.

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“…Besides, neurons in a CNN only connect to other neurons in a specific area. Based on these properties of CNN, CNN and its extension are widely used in image encryption technology [5], parallel signal processing [6] and so on [7], [8], and [9].…”

Section: Introductionmentioning

confidence: 99%

Robustness Analysis of Fuzzy Cellular Neural Network With Deviating Argument and Stochastic Disturbances

Fang

Xie

2023

IEEE Access

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Robustness analysis of fuzzy cellular neural networks with deviating arguments and stochastic disturbances is the main topic of discussion in this paper. The issue at hand is what the upper bounds of the disturbances and deviating intervals for the fuzzy cellular neural network can withstand before losing its stability. We solve these problems by using Gronwall-Bellman lemma and some inequality techniques. The theoretical results point that for an exponentially stable fuzzy cellular neural network, the perturbed fuzzy cellular neural network still keep its globally exponential stability if the upper bound of the length of deviating intervals or the intensity of stochastic disturbances is less than the upper bound derived in this paper. A number of numerical cases are offered to support the availability of conjectural values.INDEX TERMS Fuzzy cellular neural network, robustness analysis, deviating argument, stochastic disturbances.

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Complex Dynamics, Hardware Implementation and Image Encryption Application of Multiscroll Memeristive Hopfield Neural Network With a Novel Local Active Memeristor

Cited by 62 publications

References 12 publications

AC power analysis for second-order memory elements

AC power analysis for second-order memory elements

Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

Robustness Analysis of Fuzzy Cellular Neural Network With Deviating Argument and Stochastic Disturbances

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