A Class of Discrete Memristor Chaotic Maps Based on the Internal Perturbation

Yihyis, Worke Adugna; He, Shaobo; Tang, Zhouqing; Wang, Huihai

doi:10.3390/sym15081574

Cited by 3 publications

(3 citation statements)

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“…On proving the splitting vector (6), equalities (7) and ( 9) are shown to be true under condition (10). It should be emphasized that vectors (14) and (15) are not zero on the interval t ∈ [0, 2π).…”

Section: Splitting Of Harmonic Signals With Variable Amplitudementioning

confidence: 92%

“…Behavioral models describing the transfer characteristics of the Bernoulli memristors are constructed in piecewise polynomial and neural forms. The splitter is described using the expression (14) and has the structure shown in Figure 3b.…”

Section: Behavioral Modeling Of the Bernoulli Memristorsmentioning

confidence: 99%

“…Memristors have a wide field of application: electrical and radio engineering, telecommunication and control systems, computing and neuromorphic systems, robotics, cryptography, etc. Memristors are the basis of multifunctional devices, such as, in the analog field: Oscillators, variable gain amplifiers, nonlinear filters, Schmidt trigger, and rectifiers [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]; Chains with chaotic vibrations [ 11 , 12 , 13 , 14 ]; Neural networks (direct propagation, recurrent, cellular) performing modeling, filtering, signal generation, object management, and speech and image processing [ 3 , 13 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]; Neuromorphic systems that imitate the functioning of nerve tissues or biological brain structures [ 3 , 6 , 8 , 22 , 23 , 24 ]; Programmable analog integrated circuits [ 3 , 8 , 25 ]. …”

Section: Introductionmentioning

confidence: 99%

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Behavioral Modeling of Memristors under Harmonic Excitation

Solovyeva,

Serdyuk

2023

Micromachines

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Memristors are devices built on the basis of fourth passive electrical elements in nanosystems. Because of the multitude of technologies used for memristor implementation, it is not always possible to obtain analytical models of memristors. This difficulty can be overcome using behavioral modeling, which is when mathematical models are constructed according to the input–output relationships on the input and output signals. For memristor modeling, piecewise neural and polynomial models with split signals are proposed. At harmonic input signals of memristors, this study suggests that split signals should be formed using a delay line. This method produces the minimum number of split signals and, as a result, simplifies behavioral models. Simplicity helps reduce the dimension of the nonlinear approximation problem solved in behavioral modeling. Based on the proposed method, the piecewise neural and polynomial models with harmonic input signals were constructed to approximate the transfer characteristic of the memristor, in which the current dynamics are described using the Bernoulli differential equation. It is shown that the piecewise neural model based on the feedforward network ensures higher modeling accuracy at almost the same complexity as the piecewise polynomial model.

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