Clustering Using Chaotic Circuit Networks with Weighted Couplings

2019 ◽  
Vol 29 (04) ◽  
pp. 1950053 ◽  
Author(s):  
Yoko Uwate ◽  
Yuji Takamaru ◽  
Thomas Ott ◽  
Yoshifumi Nishio
Keyword(s):  
Coupling Strength ◽  
Two Dimensional ◽  
Chaotic Circuit ◽  
Distance Information ◽  
Scaling Parameters ◽  
Chaotic Circuits ◽  
Circuit Networks

In this paper, we focus on clustering phenomena in a network composed of coupled chaotic circuits. In this investigation, the coupling strength is reflected by the distance information when the chaotic circuits are placed in a two-dimensional grid. We observe various clustering phenomena in the network of coupled chaotic circuits when we vary the scaling parameters, including the coupling strength, the distance between coupled chaotic circuits and the density of the chaotic circuits.

scholarly journals A Nonvolatile Fractional Order Memristor Model and its Complex Dynamics

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 955 ◽  
Author(s):  
Wu ◽  
Wang ◽  
Iu ◽  
Shen ◽  
Zhou
Keyword(s):  
Fractional Order ◽  
Complex Dynamics ◽  
Equilibrium Points ◽  
Stable State ◽  
Electrical Characteristics ◽  
Chaotic Circuit ◽  
Stable States ◽  
Road Map ◽  
Chaotic Circuits ◽  
Integral Order

It is found that the fractional order memristor model can better simulate the characteristics of memristors and that chaotic circuits based on fractional order memristors also exhibit abundant dynamic behavior. This paper proposes an active fractional order memristor model and analyzes the electrical characteristics of the memristor via Power-Off Plot and Dynamic Road Map. We find that the fractional order memristor has continually stable states and is therefore nonvolatile. We also show that the memristor can be switched from one stable state to another under the excitation of appropriate voltage pulse. The volt–ampere hysteretic curves, frequency characteristics, and active characteristics of integral order and fractional order memristors are compared and analyzed. Based on the fractional order memristor and fractional order capacitor and inductor, we construct a chaotic circuit, of which the dynamic characteristics with respect to memristor’s parameters, fractional order α, and initial values are analyzed. The chaotic circuit has an infinite number of equilibrium points with multi-stability and exhibits coexisting bifurcations and coexisting attractors. Finally, the fractional order memristor-based chaotic circuit is verified by circuit simulations and DSP experiments.

The effect of fractionality nature in differences between computer simulation and experimental results of a chaotic circuit

Open Physics ◽  
2013 ◽  
Vol 11 (6) ◽  
Author(s):  
Salman Faraji ◽  
Mohammad Tavazoei
Keyword(s):  
Computer Simulation ◽  
Experimental Results ◽  
Chaotic Circuit ◽  
Chaotic Circuits ◽  
Simulation Results

AbstractIn practice, some differences are usually observed between computer simulation and experimental results of a chaotic circuit. In this paper, it is tried to obtain computer simulation results having more correlation with those obtained in practice by using more realistic models for chaotic circuits. This goal is achieved by considering the fractionality nature of electrical capacitors in the model of a chaotic circuit.

IMPLEMENTATION OF A LABORATORY TOOL FOR STUDYING MIXED-MODE CHAOTIC CIRCUIT

2007 ◽  
Vol 17 (10) ◽  
pp. 3633-3638 ◽  
Author(s):  
RECAI KILIÇ ◽  
BARIŞ KARAUZ
Keyword(s):  
Mixed Mode ◽  
Laboratory Work ◽  
Chaotic Circuit ◽  
Theoretical Simulation ◽  
Chaotic Circuits ◽  
Laboratory Tool

Although many authors have prepared useful papers to illustrate the existence of chaos and to enhance the reader's understanding of chaos by using theoretical, simulation or experimental setups, any chaos-based laboratory work-board has not been designed or implemented for studying chaotic circuits and systems. We designed and implemented a laboratory tool for studying mixed-mode chaotic circuits. In this paper, we will introduce this versatile laboratory tool.

IMPLEMENTING MEMRISTOR BASED CHAOTIC CIRCUITS

2010 ◽  
Vol 20 (05) ◽  
pp. 1335-1350 ◽  
Author(s):  
BHARATHWAJ MUTHUSWAMY
Keyword(s):  
Practical Implementation ◽  
Chaotic Circuit ◽  
Central Concept ◽  
Chaotic Circuits ◽  
Circuit Elements ◽  
Electric Flux

This paper provides a practical implementation of a memristor based chaotic circuit. We realize a memristor using off-the-shelf components and then construct the memristor along with the associated chaotic circuit on a breadboard. The goal is to construct a physical chaotic circuit that employs the four fundamental circuit elements — the resistor, capacitor, inductor and the memristor. The central concept behind the memristor circuit is to use an analog integrator to obtain the electric flux across the memristor and then use the flux to obtain the memristor's characterstic function.

A General Chaotic Circuit Design and Hardware Implementation via the Inductance Integrators

2019 ◽  
Vol 29 (10) ◽  
pp. 2050159
Author(s):  
Wei Xu ◽  
Ning Cao
Keyword(s):  
Circuit Design ◽  
Integrated Circuit ◽  
Output Signal ◽  
Hardware Implementation ◽  
Chaotic Circuit ◽  
Integral Circuit ◽  
Chaotic Circuits ◽  
Additive Term ◽  
Dc Resistance

This paper presents a scheme for the modified chaotic circuits based on inductance integration. In view of the fact that the DC resistance of an inductor in the circuit cannot be ignored, this way of constructing the circuits is provided that can eliminate its influence on the integral circuits. By means of cascading an inverting adder circuit and inductance integral circuit, the output signal of the integral circuit is fed back to the inverting adder circuit, and its additive term is artificially added to match the actual inductance integrated circuit to achieve integral circuit based on the actual inductor which can offset the effect of its DC resistance. In order to verify the generality of the design, the process of designing Lorenz chaotic circuit is given and its attractors can also be observed from the oscilloscope.

Controlling Hidden Dynamics and Multistability of a Class of Two-Dimensional Maps via Linear Augmentation

2021 ◽  
Vol 31 (03) ◽  
pp. 2150047
Author(s):  
Liping Zhang ◽  
Haibo Jiang ◽  
Yang Liu ◽  
Zhouchao Wei ◽  
Qinsheng Bi
Keyword(s):  
Fixed Point ◽  
Dynamical Systems ◽  
Numerical Simulations ◽  
Coupling Strength ◽  
Complex Dynamics ◽  
Discrete Dynamical Systems ◽  
Two Dimensional ◽  
Stable Fixed Point ◽  
Hidden Attractor ◽  
Hidden Attractors

This paper reports the complex dynamics of a class of two-dimensional maps containing hidden attractors via linear augmentation. Firstly, the method of linear augmentation for continuous dynamical systems is generalized to discrete dynamical systems. Then three cases of a class of two-dimensional maps that exhibit hidden dynamics, the maps with no fixed point and the maps with one stable fixed point, are studied. Our numerical simulations show the effectiveness of the linear augmentation method. As the coupling strength of the controller increases or decreases, hidden attractor can be annihilated or altered to be self-excited, and multistability of the map can be controlled to being bistable or monostable.

THE INTERPLAY OF UNCORRELATED NOISE AND SMALL WORLD EFFECT ON PATTERN FORMATION IN TWO-DIMENSIONAL COUPLED MAP LATTICES

2012 ◽  
Vol 26 (31) ◽  
pp. 1250130 ◽  
Author(s):  
DAOGUANG WANG ◽  
XIAOSHA KANG ◽  
HUAPING LÜ
Keyword(s):  
Pattern Formation ◽  
Coupling Strength ◽  
Gaussian Noise ◽  
Nearest Neighbor ◽  
Spiral Wave ◽  
Small World ◽  
Coupled Map Lattices ◽  
Two Dimensional ◽  
Excitable Systems ◽  
Generic Dynamics

By using a neuron-like map model to denote the generic dynamics of excitable systems, Gaussian-noise-induced pattern formation in the two-dimensional coupled map lattices with nearest-neighbor coupling and shortcut links has been studied. Given the appropriate initial values and parameter regions, with all nodes concerned, the functions of δ(n), χ and ℜ are introduced to analyze the evolution of pattern formation. It is found that there exists a critical εc beyond which the stable rotating spiral wave will appear. After introducing the Gaussian noise for the homogeneous ε region, different spatiotemporal stable patterns will be achieved. Additionally, the importance of the parameter I on the coupling strength C is discussed.

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