Multi-scroll Chaotic Attractors Research Articles

A novel memristor Hopfield neural network with homogeneous coexisting multi-scroll attractors

Abstract Compared to conventional single-scroll or double-scroll attractors, multi-scroll chaotic attractors possess wide potential for application due to their adjustability and complex topology. However, neural network models for generating multiple scrolls are often implemented using memristors with piecewise nonlinear functions. To further explore multi-scroll attractors with different working mechanisms,a unique memristor containing a group of hyperbolic tangent functions is designed and then applied in a three-dimensional Hopfield neural network (HNN). The proposed memristive Hopfield neural network (MHNN) has multi-scroll chaotic attractors, where the number and parity of the scrolls be changed by adjusting the control parameters of the memristor. The complex dynamical behaviors of MHNN are studied by utilizing diverse numerical modeling approaches like bifurcation diagrams, Lyapunov exponents and phase plot. In addition, the proposed MHNN also has a complicated offset boosting coexisting behavior. By selecting suitable parameters, multiple coexisting chaotic attractors could be obtained. Homogeneous coexisting multi-scroll attractors can be shifted in multiple directions including unidirectional, planar and spatial ones. Moreover, theoretically speaking, there could be an infinite number of coexisting attractors. Finally, experimental results are validated through numerical simulations and circuit experiments to confirm the feasibility of the proposed MHNN model.

Generation multi-scroll chaotic attractors using composite sine function and its application in image encryption

In this paper, a composite sine function is proposed and applied in a chaotic system, which is capable of generating definite number of chaotic attractors. The proposed composite sine function possesses infinite breakpoints, but it can produce a fixed number of scrolls by adjusting its parameters. Compared to other chaotic systems with multiple scrolls chaotic attractors, the realization circuit of the chaotic system with compound sine function allows obtaining different numbers of scrolls by adjusting only one resistance value. As a result, the circuit structure remains unchanged despite variations in the number of scrolls. Various analytical methods are applied to study the dynamical behaviors of the proposed chaotic system, including Lyapunov exponent, equilibrium point, bifurcation diagram, phase diagram, spectral entropy and C0-algorithm. Furthermore, based on the analysis of dynamical characteristics, the electronic circuits of the proposed system are given on Multisim circuit simulation software, and the multi-scroll chaotic attractors exhibit consistency with the numerical simulation results. Finally, we incorporated the proposed chaotic system into a Deoxyribonucleic acid coding algorithm for image encryption, and this method exhibits excellent encryption efficiency and high level of security.

Design of multi-scroll chaotic attractor based on a novel multi-segmented memristor and its application in medical image encryption

Medical images contain rich individual health information, making the protection of their privacy and security crucial. This study first proposes a novel multi-segment memristor based on a multi-segment linear function. Then, building upon the Sprott-B chaotic system, a mirror-symmetric memristor multi-scroll chaotic attractor (MMSCAs) is introduced by incorporating logic pulse signals and the novel multi-segment memristor. Dynamic analysis of MMSCAs is conducted from four aspects: equilibrium points, Lyapunov exponents and bifurcations, coexisting attractors, and complexity. Lyapunov exponents and bifurcation diagram analysis reveal rich dynamical behaviors in MMSCAs, including inverse period-doubling bifurcations, burst chaotic, transient chaotic, and offset boosting. MMSCAs exhibit periodic and chaotic attractors co-existing under different initial conditions, along with multi-stability and super multi-stability. Complexity analysis results indicate that MMSCAs possess higher complexity and better randomness compared to other memristor chaotic systems. The accuracy of the MMSCAs mathematical model is verified through circuit design and simulation, and the implementation of MMSCAs in the embedded domain is extended using the STM32 microcontroller. Finally, a new cryptographic system is designed by integrating MMSCAs with RNA computation and applied to medical image encryption. The security of the cryptographic system is evaluated through key space and sensitivity, histogram, and correlation, while its robustness is evaluated through resistance to cropping and noise. The analysis results demonstrate high security and strong robustness of the cryptographic system, offering a novel solution for the protection of medical image information.

Generating multi-scroll chaotic attractor in a three-dimensional memristive neuron model

The tabu learning single neuron model with external stimulus is a significant model for describing the firing activities of neurons. The current generated by a magnetic-controlled memristor has the properties of electromagnetic induction current, which can be taken as special external interference in a complex physical environment. Can this neuron model still exhibit complex and diverse firing activities when memristive current is used instead of external stimulus? To solve this puzzle, this article proposes a novel three-dimensional (3-D) memristive tabu learning neuron (MTLN) model by substituting external stimulus with memristive current, which can generate a multi-scroll chaotic attractor (MSCA) whose scrolls grow with time. The MTLN model has a line equilibrium set with different stability types relied on the initial position of the memristor internal state. Using some numerical methods, complex dynamical behaviors and scroll-growing MSCAs are uncovered. Further, a simple scroll-control scheme is designed and its effectiveness is demonstrated numerically and theoretically. The results manifest that the scrolls of the generated MSCAs can grow with time and can be also controlled by bounding the memristor internal state. In addition, an FPGA hardware platform is exploited for the 3-D MTLN model and the scroll-growing and scroll-controlled MSCAs are yielded experimentally to confirm the numerical results.

Scroll-growing/controlling chaotic attractors in cyclic Hopfield neural networks via memristive bridging

In this paper, a memristor-based neural network is proposed, which is implemented by two tri-neuron resistive-cyclic Hopfield neural networks (RC-HNNs) via memristive bridging. The memristor-bridged network has a line equilibrium set composed of infinitely many index-2 saddle-foci, but it can produce multi-scroll chaotic attractors contrary to Shil’nikov’s criterion. Complex bifurcation behaviors, scroll-growing chaotic attractors over time, and homogeneous coexisting attractors are revealed by numerical methods. Further, a scroll-control scheme is designed and scroll-controlling chaotic attractors are demonstrated numerically. The results show that the memristor-bridged network can not only generate scroll-growing chaotic attractors over time, but also produce scroll-controlling chaotic attractors by limiting the dynamic range of the internal state of the bridging memristor. Finally, an analog electronic circuit is designed for the memristor-bridged network, and PSIM circuit simulations are used to verify the numerical simulations.

A Novel Conservative Chaotic Circuit with a Cubic Root

A new conservative chaotic system with a cubic root is constructed in this paper. The system has no equilibria. An offset parameter is introduced into the system. By modulating different offset parameters and corresponding initial conditions, multiple coexisting conservative chaotic attractors can be generated consequently. By replacing the offset parameter with an appropriate sign function, multiscroll conservative chaotic attractors can be generated effectively by connecting multiple coexisting chaotic attractors based on offset boosting. Moreover, the number of scrolls tend to be infinite. Finally, a novel chaotic circuit with a cubic root is designed successfully by connecting two multipliers in opposite directions. The hardware implementation of the chaotic circuit is also completed to further verify the existence of conservative chaos and the consistency with the numerical simulations.

Solution and dynamics analysis of fractal-fractional multi-scroll Chen chaotic system based on Adomain decomposition method

In recent years, newly proposed fractal-fractional calculus operators have been used to predict the chaotic behavior of some attractors, and the new operators can capture self-similarity in the studied chaotic attractors. The results of varying with fractal order and fractional order are interesting, however, the dynamics are barely analyzed in these studies, and the law of fractal order and fractional order on the system is not clear. In this paper, the fractal-fractional differential operators with inverse operator property are redefined in the Caputo sense, and a new numerical scheme is proposed based on the sixth-order Adomain decomposition method. Then the dynamics of the new multi-scroll Chen chaotic system and its three degradation modes are analyzed completely in the fractal-fractional sense. The influence of the change of fractal order and fractional order on the chaotic system is revealed. Finally, a new physical phenomenon of the fractal-fractional chaotic system is captured by observing the coexistence of multi-scroll and two-scroll chaotic attractors of the new system through a wood-grain-like attractor basin, as well as transient chaos, chaotic jumps, and complex state transition behaviors. It is shown that fractal-fractional multi-scroll Chen system has complex and specific dynamical behaviors.

Fractional-order-induced symmetric multi-scroll chaotic attractors and double bubble bifurcations in a memristive coupled Hopfield neural networks

The multi-state memristor is a type of memristor capable of memorizing multiple conductance states. This special feature is successfully used to mimic the behavior of neural synapses and simulate the influences of electromagnetic induction and radiation in artificial neural networks such as Hopfield neural networks. This paper introduces and examines a novel fractional-order Hopfield neural network model consisting of two non-identical sub-Hopfield neural networks coupled by a multi-stable flux controlled memristor (MCFHNN). The pinched hysteresis loops of the multi-stable flux controlled memristor are analyzed via numerical simulations. The equilibrium points of MCFHNN model and their stability are investigated. The introduced MCFHNN model is solved by using Adomian Decomposition Method (ADM). By using some examination methods such as spectral entropy complexity, bifurcation diagrams, phase portraits, maximum Lyapunov exponent and basins of attraction, we analyze the dynamic behaviors of MCFHNN model associated with coupling strength, fractional-order and initial states. Numerical results demonstrate that the proposed MCFHNN model is capable to develop abundant and complicated dynamics including, chaos, multiple transient transition behaviors, double bubble bifurcations, initial-boosted behavior and symmetric multi-scroll chaotic attractors. It is found that the complexity results are in agreement with those obtained from the bifurcation diagrams. This proves that the complexity can well reflect the dynamic behaviors of MCFHNN model. In order to support theoretical and numerical results, the designed MCFHNN model is implemented with the help of an Arduino-Due microcontroller board based on the Atmel SAM3X8E ARM Cortex-M3 processor. The results are in very good agreement with those obtained from computational simulations.

Multi-ring julia fractal chaotic systems with separated scrolls and nested scrolls

Chaos and fractals are two branches of nonlinear science. However, few scholars have combined chaos and fractals for generating chaotic attractors with different topologies. The main goal of this paper is to combine fractals and chaos to generate separated multi-scroll attractors and nested multi-scroll attractors, as well as to compensate for the lack of randomness and complexity of general chaotic systems. Combining chaotic attractors with two different fractal processes, a class of multi-scroll chaotic attractors with different behaviors are generated, including multi-ring attractors, separated multi-ring attractors, and nested multi-ring attractors. The experiments of spectral entropy (SE) complexity indicates that the proposed multi-ring fractal systems have higher complexity than other chaotic systems. Furthermore, from the perspective of engineering applications, the random performance of one of the multi-ring fractal systems is tested by the NIST test suite. The results demonstrate that the multi-ring fractal system has promising randomness and is well-suited for image encryption applications. Finally, the numerical simulation results are well illustrated by the micro-controller unit (MCU) hardware experiments, which reveal the feasibility of the designed multi-ring attractors, separated multi-ring attractors, and nested attractors.

Memristive-cyclic Hopfield neural network: spatial multi-scroll chaotic attractors and spatial initial-offset coexisting behaviors

Memristive-cyclic Hopfield neural network: spatial multi-scroll chaotic attractors and spatial initial-offset coexisting behaviors

A physical memristor-based chaotic system and its application in colour image encryption scheme

This work proposes a physical memristor (TaOx) based new 4D chaotic system with 3D multi-scroll, no equilibrium point, spiking behaviour, coexistence bursting oscillation and multistability. Using this physical memristor-based chaotic system, a novel and efficient colour image encryption algorithm has been developed using a unique box scrambling method and bit-wise XOR operations. Many interesting and new dynamics of a material-based memristive chaotic system are reported here, like 3D multi-scroll chaotic attractors, bursting characteristics, multistability, a neuronal system like spiking behaviours etc using Lyapunov spectrum and bifurcation plots. It is observed that the number of scrolls is changed with the total simulation time. This novel memristive chaotic system has limit cycles with controllable spikes and bursting oscillation. In addition, the system shows chaotic bursting oscillation under a different set of parameters and initial conditions. The coexistence of the bursting phenomena is studied here. The bursting and spiking characteristic is important for material-based memristors in neuromorphic applications. 3D Chaotic multi-scroll and multistability properties make the image encryption method more efficient and secure. Such characteristics are rare in physical memristor-based chaotic systems and using this, the image encryption algorithm is also rare in recent findings. Therefore, a new secure image encryption algorithm for colour images is proposed here, based on the unique box scrambling method, bitwise XOR operation and pseudo-random number generation using the proposed memristive chaotic system. Various tests like NPCR, UACI, histogram analysis, correlation study, information entropy analysis, robustness against external noise, etc have been performed to check the algorithm’s robustness and efficiency and test the capability to resist statistical and differential attacks.

Multi-scroll Hopfield neural network under electromagnetic radiation and its brain-like coupling synchronization

Multi-scroll attractors have attracted attention because of their more complex topological structures and artificially controllable attractor structures. This paper proposes a new nonvolatile magnetic-controlled memristor and uses it to simulate the effect of membrane flux changes caused by neuronal exposure to electromagnetic radiation. A series of complex chaotic phenomena are found by plotting phase diagrams, bifurcation diagrams, attractor domains and 01 tests, including multi-scroll chaotic attractors controlled by memristors, symmetric bifurcation behavior, coexistence phenomena enhanced by initial offset. The mechanisms behind them are explained through equilibrium point analysis. A dual memristive HNN (MHNN) coupling synchronization model is proposed to simulate the synchronization between regions within the human brain. The Lyapunov function of the error is constructed to prove that this coupling synchronization scheme is ultimately bounded. The feasibility of this synchronization scheme is verified by establishing a Simulink model and conducting simulation experiments.

A general method for generating multi-scroll and multi-wing chaotic systems and its implementation of attractor reproduction

Abstract In comparison to traditional chaotic systems, the multi-scroll and multi-wing chaotic systems are more complicated. The design and execution of sophisticated multi-scroll or multi-wing chaotic attractors attract a lot of attention. However, these constructed nonlinear functions cannot be applied to extended multi-scroll and multi-wing attractors at the same time. To this end, this paper proposes a new function which can be used to generate multi-scroll and multi-wing chaotic attractors in both double-scroll and double-wing chaotic systems. Using this function, multi-scroll and multi-wing chaotic systems can be constructed directly without relying on whether the chaotic system has some symmetry (odd symmetry or even symmetry). The construction method presented is generally applicable to chaotic systems with multi-scroll and multi-wing self-excited attractors.The main point of this method is as follows: firstly, the piecewise linear (PWL) saturation function is nested within the cosine nonlinearity function , and the resulting nested COS-PWL function. Secondly, to enable the expansion of multi-wing and multi-scroll, the nested COS-PWL function is incorporated into the double-wing and double-scroll systems in different manners. The maximum Lyapunov exponent (MLE) and the bifurcation diagram route for increasing the number of wings and scrolls confirm the feasibility and effectiveness of the method. Finally, the three-element method is used to determine a Sinusoidal function, which can generate attractor self-reproduction in the corresponding dimension by replacing the state variables of the multi-scroll and multi-wing systems, so that an infinite number of coexisting attractors can be obtained by simply changing the initial values of the variables, i.e., multiple stability can be generated.

Multi-scroll chaotic attractors with multi-wing via oscillatory potential wells

The oscillatory potential well can cause the mass points in it to move chaotically, which can be considered as a physical mechanism of chaos generation. Based on this physical mechanism, the oscillatory multi-well potential with multi-concave is used to generate controllable multi-scroll chaotic attractors with multi-wing. These attractors have two kinds of topological units: scroll and wing. The topological structure of the attractor depends on the shape of the potential well, that is, the wells and the concaves correspond to the scrolls and wings of the attractor. By constructing potential wells with different shapes, we can obtain attractors with different topological structure. The number of scrolls and wings of the chaotic attractor can be controlled by the oscillation amplitude of the potential well and damping coefficient, that is, stronger oscillation or smaller damping allows the mass points to enter the higher potential wells or concaves, resulting in attractors with more scrolls or wings. As the number of scrolls or wings increases, the attractors become more complex, as expressed by Poincaré maps and quantified by the Kaplan-Yorke dimensions. Kaplan-Yorke dimensions of attractors can be adjusted continuously from 2 to 3 by the two control parameters. Finally, the results are confirmed by Multisim simulation circuit and actual analog circuit.

The behavior of hidden bifurcation in 2D scroll via saturated function series controlled by a coefficient harmonic linearization method

Abstract In this article, the behavior of hidden bifurcation in a two-dimensional (2D) scroll via saturated function series controlled by the coefficient harmonic linearization method is presented. A saturated function series approach for chaos generation. The systematic saturated function series methodicalness improved here can make multi-scroll and grid scroll chaotic attractors from a 3D linear autonomous system with a plain saturated function series supervisor. We have used a hidden bifurcation method in grid scroll., where the method of hidden bifurcation presented by Menacer, et al. in (2016) for Chua multi-scroll attractors. This additional parameter, which is absent from the initial problem, is perfectly adapted to unfold the structure of the multispiral chaotic attractor. The novelty of this article is twofold: first, the saturated function series model for hidden bifurcation in a 2 – D scroll; and second, the control of hidden bifurcation behavior by the value of the harmonic coefficient k 3 {k}_{3} .

Symmetric multi-scroll attractors in magnetized Hopfield neural network under pulse controlled memristor and pulse current stimulation

In this paper, a new method for generating symmetric multi-scroll attractors is proposed based on a magnetized Hopfield neural network (HNN). Firstly, the mathematical model of magnetized HNN by introducing an original memristor to describe the effect of electromagnetic radiation is established, and its dynamic characteristics are analyzed. Then, utilizing two types of multi-level-logic pulse functions, the complex attractor structure changes of the magnetized HNN under external pulse controlled memristor, pulse current stimulation and their combined effects are discussed respectively. The numerical simulation results show that the magnetized HNN under pulse controlled memristor or/and pulse current stimulation can produce multi-scroll chaotic and multi-scroll periodic strange attractors with symmetric structure. The number of attractors can be changed by setting the different series of multi-level-logic pulse functions. Finally, the magnetized HNN circuit under pulse controlled memristor or/and pulse current stimulation is realized by using discrete analog electronic devices. The experimental results verify the validity of the numerical simulation results.

Two-dimensional non-autonomous neuron model with parameter-controlled multi-scroll chaotic attractors

This work presents a two-dimensional (2-D) non-autonomous tabu learning single neuron (TLSN) model based on sinusoidal activation function (SAF), which can generate a class of multi-scroll chaotic attractors with parameters controlling the number of scrolls. The SAF-based TLSN (SAF-TLSN) model has a periodically alterable equilibrium point closely related to the model parameters. Its equilibrium trajectories with different stability types can be distributed in the phase plane, resulting in the generation of multi-scroll chaotic attractors. The multi-scroll chaotic attractors and their dynamical behaviors are investigated by phase plane portrait, maximal Lyapunov exponent, bifurcation diagram, and cross section. The numerical results demonstrate that each scroll of multi-scroll chaotic attractors is associated with the equilibrium trajectories composed of infinitely many unstable equilibrium points, and the scroll distributions of multi-scroll chaotic attractors are confined to the distribution ranges of equilibrium trajectories that are controlled by the model parameters. Besides, using a digital hardware platform, the SAF-TLSN model is implemented and the multi-scroll chaotic attractors with different scrolls are obtained experimentally to verify the numerical ones.

Generating n-Scroll Chaotic Attractors From a Memristor-Based Magnetized Hopfield Neural Network

This brief presents a novel method to generate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -scroll chaotic attractors. First, a magnetized Hopfield neural network (HNN) with three neurons is modeled by introducing an improved multi-piecewise memristor to describe the effect of electromagnetic induction. Theoretical analysis and numerical simulation show that the memristor-based magnetized HNN can generate multi-scroll chaotic attractors with arbitrary number of scrolls. The number of scrolls can be easily changed by adjusting the memristor control parameters. Besides, complex initial offset boosting behavior is revealed from the magnetized HNN. Finally, a magnetized HNN circuit is designed and various typical attractors are verified.

Coexistence of multiscroll chaotic attractors in two coupled inertial Hopfield neurons: numerical simulations and experiment

In this contribution, we consider the dynamics of a pair of coupled inertial neurons with hyperbolic tangent activation function. The two inertial neurons are coupled by adding to each one’s amplitude a perturbation proportional to the other one. The model is governed by a fourth-order autonomous system with hyperbolic tangent nonlinearities. The analysis of the coupled system yields nine equilibrium points some of which experience Hopf type bifurcation. When adjusting the coupling coefficients, striking nonlinear patterns are disclosed such as the coexistence of numerous bifurcation branches, merging crisis, multiple Hopf bifurcations, coexisting self-excited motions (e.g. two coexisting double-scroll chaos, four coexisting period-n cycles, four coexisting single-scroll chaos), and four-scroll chaotic attractors. These latter features are diagnosed with the help of classic numerical tools (e.g. 1D and 2D maximum Lyapunov exponent diagrams, 1D bifurcation diagrams of local peaks of variables, frequency spectrum plots, phase space trajectory plots, and attraction basins). The analogue electronic circuit design of the coupled inertial neurons system is carried out and simulated in PSpice to verify diverse types of features reported during the theoretical study. One of main achievements of the present article is that the coupling of inertial neurons can be regarded as an alternative scheme to obtain multiscroll chaotic signals.

Generation of multiscroll chaotic attractors of a finance system with mirror symmetry

Generation of multiscroll chaotic attractors of a finance system with mirror symmetry