Chaotic memristor

Driscoll, Tom; Pershin, Yuriy V.; Basov, D. N.; Ventra, Massimiliano Di

doi:10.1007/s00339-011-6318-z

Cited by 55 publications

(20 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A memristor can lead to chaotic behavior when connected to a power source or when the Chua diode is replaced with a memristor [3,4,23,24]. In [24] several nonlinear oscillators utilized monotone increasing piecewise-linear memristors with passive characteristics, and in [2] a memristor was used instead of the Chua diode in Chua's circuit.…”

Section: Introductionmentioning

confidence: 99%

Behavior learning of a memristor-based chaotic circuit by extreme learning machines

Uçar¹,

Yavşan²

2016

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:As the behavior of a chaotic Chua's circuit is nonstationary and inherently noisy, it is regarded as one of the most challenging applications. One of the fundamental problems in the prediction of the behavior of a chaotic Chua's circuit is to model the circuit with high accuracy. The current paper presents a novel method based on multiple extreme learning machine (ELM) models to learn the chaotic behavior of the four elements canonical Chua's circuit containing a memristor instead of a nonlinear resistor only by using the state variables as the input. In the proposed method four ELM models are used to estimate the state variables of the circuit. ELMs are first trained by using the data spoilt by noise obtained from MATLAB models of a memristor and Chua's circuit. A multistep-ahead prediction is then carried out by the trained ELMs in the autonomous mode. All attractors of the circuit are finally reconstructed by the outputs of the models. The results of the four ELMs are compared to those of multiple linear regressors (MLRs) and support vector machines (SVMs) in terms of scatter plots, power spectral density, training time, prediction time, and some statistical error measures. Extensive numerical simulations results show that the proposed system exhibits a highly accurate multistep iterated prediction consisting of 1104 steps of the chaotic circuit. Consequently, the proposed model can be considered a promising and powerful tool for modeling and predicting the behavior of Chua's circuit with excellent performance, reducing training time, testing time, and practically realization probability.

show abstract

Section: Introductionmentioning

confidence: 99%

Behavior learning of a memristor-based chaotic circuit by extreme learning machines

Uçar¹,

Yavşan²

2016

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…In this case and many others, memristor represents nonlinear function (eg Chua's diode) and together with other elements (eg resistors, capacitors and inductors) is possible to realize a simple chaotic oscillators [4,5,8,11,12,14]. Many others authors also deals with modeling and realization of memristor [15][16][17][18][19][20][21]. Nevertheless, this paper is not concentrated on memristor elements realization itself.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Synthesis of Chaotic Circuits using Memristor Properties

Hrubos

Götthans

2014

Journal of Electrical Engineering

View full text Add to dashboard Cite

This paper provides an innovative practical realization of a memristor based chaotic circuit. The first part discusses the mathematical analysis of the proposed system, including calculation of an eigenvalues, bifurcation diagram and largest Lyapunov exponents. Another parts deal with circuitry realization and the influence of parasitic properties of active elements. The circuit simulations obtained by PSpice environment and the practical measurement results on a breadboard are presented in the last part of this paper. The main aim of this work is an innovative realization of the memristor based chaotic circuit with one type of energy-storage element (linear passive capacitor) and with simpler construction in comparison to other circuits. The next contribution consists in verification of designed circuit with respect to influence of parasitic properties of active elements to chaos destruction. K e y w o r d s: differential equations,

show abstract

“…This unifying description is a source of inspiration for novel digital and analog applications 18,20,21 and allows us to bridge apparently different areas of research. 3 However, despite the wealth of applications and new ideas these concepts have generated, it is nonetheless important to stress that so far these memory elements have been discussed only within their classical circuit theory definition, with quantum mechanics entering at best in the microscopic parameters that determine the state variables responsible for memory 3,17,22 . However, it seems that these features are common at the nanoscale where the dynamical properties of electrons and ions are likely to depend on the history of the system, at least within certain time scales 23,24 .…”

Section: Introductionmentioning

confidence: 99%

Lagrange formalism of memory circuit elements: Classical and quantum formulations

2012

View full text Add to dashboard Cite

The general Lagrange-Euler formalism for the three memory circuit elements, namely, memristive, memcapacitive, and meminductive systems, is introduced. In addition, mutual meminductance, i.e. mutual inductance with a state depending on the past evolution of the system, is defined. The Lagrange-Euler formalism for a general circuit network, the related work-energy theorem, and the generalized Joule's first law are also obtained. Examples of this formalism applied to specific circuits are provided, and the corresponding Hamiltonian and its quantization for the case of non-dissipative elements are discussed. The notion of memory quanta, the quantum excitations of the memory degrees of freedom, is presented. Specific examples are used to show that the coupling between these quanta and the well-known charge quanta can lead to a splitting of degenerate levels and to other experimentally observable quantum effects.

show abstract

Chaotic memristor

Cited by 55 publications

References 38 publications

Behavior learning of a memristor-based chaotic circuit by extreme learning machines

Behavior learning of a memristor-based chaotic circuit by extreme learning machines

Analysis and Synthesis of Chaotic Circuits using Memristor Properties

Lagrange formalism of memory circuit elements: Classical and quantum formulations

Contact Info

Product

Resources

About