Memristor Model Comparison

Ascoli, Alon; Corinto, Fernando; Senger, Vanessa; Tetzlaff, Ronald

doi:10.1109/mcas.2013.2256272

Cited by 173 publications

(116 citation statements)

References 24 publications

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“…The parameter 'p' determines the rate of decrease of the window function as the state variable approaches any of its two bounds, and 'j' sets the maximum value of the window function itself, which may here exceed the unitary value [13].…”

Section: Window Function Modelsmentioning

confidence: 99%

“…Reference [13] provides a comparison among them, it is shown that neither Joglekar's nor Prodomakis' models were able to reproduce the dynamics shown by the reference model. It is noted that all of them are functions only in the state variable (except Biolek's, which is also function of the sign, not the magnitude, of the current).…”

Section: Window Function Modelsmentioning

confidence: 99%

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Memristor model based on fuzzy window function

Abdel-Kader

Abuelenin

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-Memristor (memory-resistor) is the fourth passive circuit element. We introduce a memristor model based on a fuzzy logic window function. Fuzzy models are flexible, which enables the capture of the pinched hysteresis behavior of the memristor. The introduced fuzzy model avoids common problems associated with window-function based memristor models, such as the terminal state problem, and the symmetry issues. The model captures the memristor behavior with a simple rule-base which gives an insight of how memristors work. Because of the flexibility offered by the fuzzy system, shape and distribution of input and output membership functions can be tuned to capture the behavior of various real memristors.

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Section: Window Function Modelsmentioning

confidence: 99%

Section: Window Function Modelsmentioning

confidence: 99%

Memristor model based on fuzzy window function

Abdel-Kader

Abuelenin

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…Afterwards, the direct physical realization of memristors as the fourth basic circuit element opens new vistas and stimulates research interests in RF/microwave circuits. 14 Extensive analysis of the memristor properties and the detailed fingerprints has been summarized by different models, [15][16][17] and Simulation Program with Integrated Circuit Emphasis (SPICE) has been used to build up reliable circuit models to explore and simulate application circuits which exploit potentials of memristors. 18,19 However, no research has been found that the memristor is one of the sources of intermodulation in microwave circuits.…”

Section: Introductionmentioning

confidence: 99%

Asymmetrical passive intermodulation distortions of memristors with mathematical behavior models

Jin

Wang

et al. 2016

AIP Advances

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A rigorous mathematical explanation and accurate numerical prediction for asymmetrical passive intermodulation (PIM) distortions of memristors are investigated in this article. This theoretical explanation is based on behavior models of memristors representing the interrelation between terminated voltages and currents. The simulated single-tone and two-tone signal spectrums for extremely low-frequency (Hz) and microwave (GHz) applications verify our proposed mathematical approach and the new discovery of asymmetrical PIM distortions. This presented method provides an innovative choice to model and simulate the external performance of circuits and systems with asymmetrical PIM distortions in the future.

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“…Up until now, most of the effort towards a universal model suitable for MDs has been focused in a physics based approach, biased by the Hewlett Packard (HP) team reported results [4]. It has been ascertained that the accuracy of these models is questionable for some applications [5], thus requiring better solutions and research effort. Given the wide range of materials and devices exhibiting memristive effects, it seems adequate to concentrate in a single modeling approach.…”

Section: Introductionmentioning

confidence: 99%

Memristor state-space embedding

Dghais

Alves

Mendes

et al. 2015

2015 European Conference on Circuit Theory and Design (ECCTD)

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Abstract-This paper presents a procedure for the determination of the dimensionality of the state space of a memristive device. The state space dimensionality of a device corresponds to the minimum number of time delayed values/derivatives of the voltage and current required to represent the device dynamics for a specified set of inputs. The algorithm is based on the observed time domain voltage-current (i.e. input-output) data which is obtained by measurement. The determination of the state space dimensionality is important to achieve a single-valued input-output multivariate mapping between the device outputs as a function of the embedding state variables. In this paper, this will be accomplished using an embedding technique, based on the false nearest neighbor principle.

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Memristor Model Comparison

Cited by 173 publications

References 24 publications

Memristor model based on fuzzy window function

Memristor model based on fuzzy window function

Asymmetrical passive intermodulation distortions of memristors with mathematical behavior models

Memristor state-space embedding

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