High-dimensional Chaotic Systems Research Articles

Performance of Finite Precision on Discrete Chaotic Map Based on a Feedback Shift Register

The scintillating technological improvements have changed the process of communication in all parts of the world. Shopping, banking, instant communication, and so on can be operated online and do not care about the linkage of those Internet communications. Because of simple chaotic structure, discrete nature, less arithmetic computation, and high complexity, low-dimensional chaotic systems such as a logistic map and tent map are more attractive than a high-dimensional chaotic system. To overcome the disadvantages of a low-dimensional chaotic map with a finite precision in chaos-based application, the chaotic map with a feedback shift register (CMFSR) is proposed. The related properties and performance associated with CMFSR are analysed. The relationship among the precision of the system, the architecture, and the performance are discussed. The experiments about new pseudorandom number generator (PRNG) based on CMFSR show that our scheme is simple, secure, and easy to accomplish. Experiments show that the proposed architecture of CMFSR is secure for random number generation.

2D Non-adjacent coupled map lattice with q and its applications in image encryption

In this paper, a novel high dimensional spatiotemporal chaotic system based on 2D non-adjacent coupled map lattice (2DNACML) model is proposed. Each local lattice is influenced by other random lattices in different dimensions and the local map is defined as a fractional-like style. From the theoretical analysis and numerical simulation, it is found that ranges of system parameters are expanded and unfixed in the novel 2DNACML model. As a result, the changed system is improved and been applied in the image encryption with a mixed scrambling scheme with chaotic method and knights tour method and an efficient diffusion scheme for different images to show the effectiveness of the proposed system.

Bayesian framework for simulation of dynamical systems from multidimensional data using recurrent neural network.

We suggest a new method for building data-driven dynamical models from observed multidimensional time series. The method is based on a recurrent neural network with specific structure, which allows for the joint reconstruction of both a low-dimensional embedding for dynamical components in the data and an evolution operator. The key link of the method is a Bayesian optimization of both model structure and the hypothesis about the data generating law, which is needed for constructing the cost function for model learning. First, the performance of the method is successfully tested in the situation when a signal from a low-dimensional dynamical system is hidden in noisy multidimensional observations. Second, the method is used for building the data-driven model of the low frequency variability (LFV) in the quasigeostrophic model of the Earth's midlatitude atmosphere-a high-dimensional chaotic system. It is demonstrated that the key regimes of the atmospheric LFV are reproduced correctly in data simulations by means of the obtained model.

On The Synchronization of Van Der Pol-Duffing Oscillator

The most well known property of chaotic systems is their sensitivity to initial conditions. In this work the criterion presented in literature for synchronizing two chaotic systems is applied to a system consisting of two Van der Pol-Duffing oscillators. First, the route to chaos is investigated for the Duffing oscillator. Furthermore, the Lyapunov function approach is used to design a high dimensional chaotic system. Then certain subsystems of a nonlinear chaotic system are synchronized by linking them with a common signal. Synchronization has been observed when there exists an asymptotic stability and an appropriate Lyapunov function, also by computing all the Lyapunov exponents and Kolmogorov entropy.

A new class of Hamiltonian conservative chaotic systems with multistability and design of pseudo-random number generator

• A new modeling method is employed to develop a class of high dimensional Hamiltonian conservative chaotic systems. • A new conservative hyperchaotic system is proposed and exhibits some behaviour previously unobserved. • Multistability including infinite-orbit and infinite-scroll is observed in the proposed systems. • For encryption, pseudo-random digital signal generators based on conservative chaotic systems are developed. Compared to dissipative chaotic system, conservative chaotic systems have some attractive features valuable for engineering applications, such as security communication. This paper proposes a method for constructing new high dimensional conservative chaotic systems, for instance 4 dimensional conservative chaotic systems and 5 dimensional conservative hyper-chaotic systems. The dynamic properties of the constructed systems are verified by the dynamical evolution map, equilibrium points, Lyapunov dimension, the volume of flow and the Hamiltonian energy. An interesting co-existence of quasi-period, chaos and hyper-chaos flows are observed in the developed conservative chaotic systems. With trigonometric transformation, parallel conservative chaotic systems are achieved with infinitely many scrolls. To accommodate the engineering application requirement, the security communication is considered as a case study. The proposed conservative chaotic systems passed the validation National Institute of Standard Statistic Test criteria for the pseudo-random property , which is the critical factor of security communication. Furthermore, the pseudo-random signal generator is developed using the Field Programmable Gate Array technique. Due to the complicated topology structure, such digital implementation was not commonly reported in literatures, instead most conservative chaotic systems were validated only by analog circuit simulation.

Multi-process image encryption scheme based on compressed sensing and multi-dimensional chaotic system

With the rapid development of computer science, the storage and dissemination of information are often carried out between various types of computer hardwares and various networks. The traditional information encryption scheme has gradually disappeared. Therefore, computer-based information encryption algorithms have gradually become a research hotspot in recent years. By combining the theory of wavelet packet transform, compressed sensing and chaotic system, a multi-process image encryption scheme based on compressed sensing and multi-dimensional chaotic system is proposed. The encryption scheme implements compression and encryption for grayscale images and corresponding decompression and decryption process. The wavelet packet transform theory is applied to the image preprocessing stage to perform wavelet packet decomposition on the original image. At the same time, the image signal components obtained by the decomposition are classified according to the threshold processing method, and the characteristics of the image signal components are processed in the subsequent processing. They are compressed, encrypted, or reserved in a differentiated manner. In the image compression stage, by introducing the compressed sensing algorithm to overcome the shortcomings of the traditional Nyquist sampling theorem, such as high sampling cost and low reconstruction quality, the compression efficiency and compression quality are improved while the ciphertext image reconstruction quality is guaranteed. In the image encryption stage, the encryption scheme combines multi-class and multi-dimensional chaotic systems to confuse and scramble the related image signal components, and introduces a high-dimensional chaotic system to make the encryption scheme have a large enough key space to further enhance the ciphertext image reliability. Finally, the complete reconstruction of the original image is achieved by applying the inverse of compression, encryption and wavelet packet transform. The simulation results show that the image encryption scheme effectively protects the basic information about ciphertext images by virtue of algorithm robustness against external interference, and does not reveal any useful information when dealing with cracking methods such as plaintext attacks. In addition, the information entropy and correlation coefficient of ciphertext images encrypted by this encryption scheme are closer to ideal values than those of the encryption algorithm in the references, and its encryption performance is significantly improved.

Analysis of the Time Series Generated by a New High-Dimensional Discrete Chaotic System

The chaotic behavior of low-dimensional digital chaotic systems is seriously degraded, and the output sequence has a short period. In this study, a digital sequence generator based on a high-dimensional chaotic system is proposed to ensure performance and security. The proposed generator has low resource consumption, and the digital pseudo-random output sequence has a large period. To avoid the nonchaotic state, the multistability in the high-dimensional discrete chaotic system is analyzed. The statistical performance of the output sequence of the proposed digital high-dimensional chaotic system is evaluated, and the results demonstrate that it is a suitable candidate for a long-period pseudo-random sequence generator.

Parameter estimation of a complex chaotic system with unknown initial values

The parameter estimation of a chaotic system is an important issue in nonlinear science, and it has gained increased attention in recent years. However, the existing estimation methods must be based on the initial values known in the original system. Yet the initial values cannot be obtained in many cases, which is not conducive to the reconstruction and control of chaotic systems. In addition, these methods are unable to provide enough precision for high-dimensional complex chaotic systems. In this paper, a parameter estimation method with unknown initial values is developed, and a new algorithm called the chaos behaved particle swarm optimization algorithm is proposed. Three highlights, namely chaos initialization, special inertia weight and chaos search, are introduced into the algorithm. The simulation experiments are carried out for three complex chaotic systems, including two typical fractional-order hyperchaotic systems and a fractional-order multi-directional multi-scroll chaotic attractor system. Based on the results, the method proposed in this paper demonstrates more effectiveness and advantages than the other four existing algorithms.

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model.

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Periodic orbits of diffusionless Lorenz system

The strange attractor of a chaotic system is composed of numerous periodic orbits densely covered. The periodic orbit is the simplest invariant set except for the fixed point in the nonlinear dynamic system, it not only reflects all the characteristics of the chaotic motion, but also is closely related to the amplitude generation and change of chaotic system. Therefore, it is of great significance to obtain the periodic orbits in order to analyze the dynamical behaviors of the complex system. In this paper, we study the periodic orbits of the diffusionless Lorenz equations which are derived in the limit of high Rayleigh and Prandtl numbers. A new approach to establishing one-dimensional symbolic dynamics is proposed, and the periodic orbits based on a topological structure are systematically calculated. We use the variational method to locate the cycles, which is proposed to explore the periodic orbits in high-dimensional chaotic systems. The method not only preserves the robustness characteristics of most of other methods, such as the Newton descent method and multipoint shooting method, but it also has the characteristics of fast convergence when the search process is close to the real cycle in practice. In order to apply the method, a rough loop guess must be made first based on the entire topology for the cycle to be searched, and then the variational algorithm will bring the initial loop guess to evolving toward the real periodic orbit in the system. In the calculations, the Newton descent method is used to achieve stability. Two cycles can be used as basic building blocks for initialization, searching for more complex cycles with multiple circuits around the two fixed points requires more delicate initial conditions; otherwise, it will probably lead to nonconvergence. We can initialize the loop guess for longer cycles constructed by cutting and gluing the short, known cycles. For this system, such a method yields quite a good systematic initial guess for longer cycles. Even if we deform the orbit manually into a closed loop, the variational method still shows its powerfulness for good convergence. The topological classification based on the entire orbital structure is shown to be effective. Furthermore, the deformation of periodic orbits with the change of parameters is discussed, which provides a route to the periods of cycles. The present research may provide a method of performing systematic calculation and classification of periodic orbits in other similar chaotic systems.

Detecting unstable periodic orbits in chaotic time series using synchronization.

An alternative approach of detecting unstable periodic orbits in chaotic time series is proposed using synchronization techniques. A master-slave synchronization scheme is developed, in which the chaotic system drives a system of harmonic oscillators through a proper coupling condition. The proposed scheme is designed so that the power of the coupling signal exhibits notches that drop to zero once the system approaches an unstable orbit yielding an explicit indication of the presence of a periodic motion. The results shows that the proposed approach is particularly suitable in practical situations, where the time series is short and noisy, or it is obtained from high-dimensional chaotic systems.

Reduced-order description of transient instabilities and computation of finite-time Lyapunov exponents.

High-dimensional chaotic dynamical systems can exhibit strongly transient features. These are often associated with instabilities that have a finite-time duration. Because of the finite-time character of these transient events, their detection through infinite-time methods, e.g., long term averages, Lyapunov exponents or information about the statistical steady-state, is not possible. Here, we utilize a recently developed framework, the Optimally Time-Dependent (OTD) modes, to extract a time-dependent subspace that spans the modes associated with transient features associated with finite-time instabilities. As the main result, we prove that the OTD modes, under appropriate conditions, converge exponentially fast to the eigendirections of the Cauchy-Green tensor associated with the most intense finite-time instabilities. Based on this observation, we develop a reduced-order method for the computation of finite-time Lyapunov exponents (FTLE) and vectors. In high-dimensional systems, the computational cost of the reduced-order method is orders of magnitude lower than the full FTLE computation. We demonstrate the validity of the theoretical findings on two numerical examples.

Fluctuations, response, and resonances in a simple atmospheric model

We study the response of a simple quasi-geostrophic barotropic model of the atmosphere to various classes of perturbations affecting its forcing and its dissipation using the formalism of the Ruelle response theory. We investigate the geometry of such perturbations by constructing the covariant Lyapunov vectors of the unperturbed system and discover in one specific case–orographic forcing–a substantial projection of the forcing onto the stable directions of the flow. This results into a resonant response shaped as a Rossby-like wave that has no resemblance to the unforced variability in the same range of spatial and temporal scales. Such a climatic surprise corresponds to a violation of the fluctuation–dissipation theorem, in agreement with the basic tenets of nonequilibrium statistical mechanics. The resonance can be attributed to a specific group of rarely visited unstable periodic orbits of the unperturbed system. Our results reinforce the idea of using basic methods of nonequilibrium statistical mechanics and high-dimensional chaotic dynamical systems to approach the problem of understanding climate dynamics.

Optical noise-free image encryption based on quick response code and high dimension chaotic system in gyrator transform domain

A novel optical image encryption scheme is proposed based on quick response code and high dimension chaotic system, where only the intensity distribution of encoded information is recorded as ciphertext. Initially, the quick response code is engendered from the plain image and placed in the input plane of the double random phase encoding architecture. Then, the code is encrypted to the ciphertext with noise-like distribution by using two cascaded gyrator transforms. In the process of encryption, the parameters such as rotation angles and random phase masks are generated as interim variables and functions based on Chen system. A new phase retrieval algorithm is designed to reconstruct the initial quick response code in the process of decryption, in which a priori information such as three position detection patterns is used as the support constraint. The original image can be obtained without any energy loss by scanning the decrypted code with mobile devices. The ciphertext image is the real-valued function which is more convenient for storing and transmitting. Meanwhile, the security of the proposed scheme is enhanced greatly due to high sensitivity of initial values of Chen system. Extensive cryptanalysis and simulation have performed to demonstrate the feasibility and effectiveness of the proposed scheme.

A hyper-chaos-based image encryption algorithm using pixel-level permutation and bit-level permutation

Recently, a number of chaos-based image encryption algorithms that use low-dimensional chaotic map and permutation-diffusion architecture have been proposed. However, low-dimensional chaotic map is less safe than high-dimensional chaotic system. And permutation process is independent of plaintext and diffusion process. Therefore, they cannot resist efficiently the chosen-plaintext attack and chosen-ciphertext attack. In this paper, we propose a hyper-chaos-based image encryption algorithm. The algorithm adopts a 5-D multi-wing hyper-chaotic system, and the key stream generated by hyper-chaotic system is related to the original image. Then, pixel-level permutation and bit-level permutation are employed to strengthen security of the cryptosystem. Finally, a diffusion operation is employed to change pixels. Theoretical analysis and numerical simulations demonstrate that the proposed algorithm is secure and reliable for image encryption.

A novel pseudorandom number generator based on pseudorandomly enhanced logistic map

In last years, low-dimensional and high-dimensional chaotic systems have been implemented in cryptography. The efficiency and performance of these nonlinear systems play an important role in limited hardware implementations. In this context, low-dimensional chaotic systems are more attractive than high-dimensional chaotic systems to produce the pseudorandom key stream used for encryption purposes. Although low-dimensional chaotic maps present some security disadvantages when they are used in cryptography, they are highly attractive due its simple structure, discrete nature, less arithmetic operations, high output processing, and relatively easy to implement in a digital system. In this paper, we proposed both a pseudorandomly enhanced logistic map (PELM) and its application in a novel pseudorandom number generator (PRNG) algorithm, which produces pseudorandom stream with excellent statistical properties. The proposed PELM is compared with logistic map by using histograms and Lyapunov exponents to show its higher benefits in pseudorandom number generator. In contrast to recent schemes in the literature, we present a comprehensive security analysis over the proposed pseudorandom number generator based on pseudorandomly enhanced logistic map (PRNG–PELM) from a cryptographic point of view to show its potential use in secure communications. In addition, the randomness of the PRNG–PELM is verified with the most complete random test suit of National Institute of Standards and Technology (NIST 800-22) and with TestU01. Based on security results, few arithmetic operations required, and high output rate, the proposed PRNG–PELM scheme can be implemented in secure encryption applications, even in embedded systems with limited hardware resources.

An image encryption scheme combining chaos with cycle operation for DNA sequences

An image encryption scheme is proposed using high-dimensional chaotic systems and cycle operation for DNA sequences. In the scheme, the pixels of the original image are encoded randomly with the DNA coding rule controlled by a key stream produced from Chen’s hyper-chaos. In addition to confusion on the DNA sequence matrix with Lorenz system, a cycle operation for DNA sequences is projected to diffuse the pixel values of the image. In order to enhance the diffusion effect, a bitwise exclusive-OR operation is carried out for the decoded matrices with a binary key stream, and then the cipher-image is obtained. Simulation results demonstrate that the proposed image encryption scheme with acceptable robustness is secure against exhaustive attack, statistical attack and differential attack.

Chaotic Map Construction from Common Nonlinearities and Microcontroller Implementations

This work presents novel discrete-time chaotic systems with some known physical system nonlinearities. Dynamic behaviors of the models are examined with numerical methods and Arduino microcontroller-based experimental studies. Many new chaotic maps are generated in the form of [Formula: see text] and high-dimensional chaotic systems are obtained by weak coupling or cross-coupling the same or different chaotic maps. An application of the chaotic maps is realized with Arduino for chaotic pulse width modulation to drive electrical machines. It is expected that the new chaotic maps and their microcontroller implementations will facilitate practical chaos-based applications in different fields.

Finite-Time Synchronization for High-Dimensional Chaotic Systems and Its Application to Secure Communication

The finite-time synchronization for the high-dimensional chaotic system is studied. A method is derived from the finite-time stability theory and adaptive control technique. To show the wider applicability of our method, an illustration is given using four-dimensional (4D) hyperchaotic systems. Numerical simulations are also used to verify the effectiveness of the technique. Then, the synchronization is applied to secure communication through chaos masking. Simulation results show that the two high-dimensional chaotic systems can realize monotonous synchronization, and the information signal, which is masked, can be recovered undistortedly.