Chaotic Analysis Research Articles

Traffic Prediction Scheme based on Chaotic Models in Wireless Networks

Based on the local support vector algorithm of chaotic time series analysis, the Hannan-Quinn information criterion and SAX symbolization are introduced. Then a novel prediction algorithm is proposed, which is successfully applied to the prediction of wireless network traffic. For the correct prediction problems of short-term flow with smaller data set size, the weakness of the algorithms during model construction is analyzed by study and comparison to LDK prediction algorithm. It is verified the Hannan-Quinn information principle can be used to calculate the number of neighbor points to replace pervious empirical method, which uses the number of neighbor points to acquire more accurate prediction model. Finally, actual flow data is applied to confirm the accuracy rate of the proposed algorithm LSDHQ. It is testified by our experiments that it also has higher performance in adaptability than that of LSDHQ algorithm.

Research on Classification and Redundant Information Filtering of Massive Data in Big Database

The precise classification of massive information in big database was researched in this paper, and also the redundant information as the interference should be filtered in the subject. According to the traditional data classification method, the frequency points were concentrated and the data classification frequent points were not easy to be eliminated. The nodes classification technology with low self-adaptive property refused the nodes in high disturbance and in the deep attenuation parts, and then the classification precision and the immunity of the disturbance property were limited greatly. A new optimum data classification method and the redundant information model were proposed based on the chaotic probability analysis. The classification error rates was mapped as a probability density function based on the channel mapping function method, the classification probability was allocated with this probability density function. The random series which could reflect the essential feature was produced based on the chaotic probability analysis method which could meet to the demands of the random frequency classification. And the data clustering and optimization classification was realized finally. Simulation was taken with the KDD_CUP2009 experimental big database, and simulation result shows that the proposed method can classify each type of the data effectively. The performance of the data classification is perfect, comparing to the traditional neural net fuzzy c-means method, the classification precision rate was improved by 17.8% It show that the model and algorithm has excellent classification performance and can be taken in the application such as data mining, fault diagnosis and target recognition as engineering practice.

A New Chaotic System Family Dynamics Analysis and Circuit Implementation

A new three-dimensional chaotic system is presented in this paper. Some basic dynamical Properties of this chaotic system are investigated by means of Poincaré mapping, Lyapunov exponents and bifurcation diagram. The dynamical behaviours of this system are proved not only by performing numerical simulation and brief theoretical analysis but also by conducting an electronic circuit implementation. It is new physical phenomenon that the Poincaré mapping of this system is a group of parallel lines.

Chaotic Characteristic Analysis of Concrete Fracture System Based on Lyapunov Exponent Stability Criterion

According to the research on whole curves of strain-stress of concrete materials, the paper illustrates in evidence features of stages and shows that the discrete feature of curve often occurs in strain-softening stage. After chaotic dynamic analysis of testing datas, it presents that system of whole process of strain-stress evolves from ordered steady state to low chaotic state and then to high chaotic state along with increase of compressive strength. The linear relationship of strain-stress grows evident and the system evolves from strong ordered steady state to low chaos state. The strain-stress system before compressive strength peak is basically in weak chaotic state. Theis proposed to be the stability criterion of concrete features in different stress stages, and the is regarded as the representative value of the system stability degree. The calculation of example shows that the stability criterion definited by the proposed method is consistent with the actural situation.

Flow noise characterization of gas–liquid two-phase flow based on acoustic emission

Flow noise of gas–liquid two-phase flow in horizontal pipeline was detected by using the acoustic emission technique (AE); signals were processed by wavelet transform and chaotic analysis. Conclusions were drawn that stratified flow, annular flow and their transition can be divided clearly through multi-scale energy distribution of flow noise, and that dynamic characteristic of flow pattern transition from stratified flow to annular flow, which is described via correlation dimension, acts in accordance with that of annular flow. The dynamic characteristic of the transition condition has already been consistent with that of the annular flow, but due to the low gas flow rate, the energy of the hydrodynamic noise was not enough to reach the complete annular flow pattern. Results were in accordance with experimental facts. Flow noise reflects the complexity of gas–liquid two-phase flow by means of multi-scale energy distribution and chaotic features. Consequently, flow noise based on acoustic emission is a novel and promising point for researching gas–liquid two-phase flow.

Multiscale complex network constructed from chaotic time series

Complex networks play a significant role in modern complex systems sciences in that they allow for the quantitative analysis of the structural properties of systems composed of different interacting entities. Recently, intensive efforts have been made to apply network-based concepts also for the analysis of time series. In this reported work, multiscale complex networks from the chaotic time series are constructed by a decomposition strategy. Since different components exist in the time series, it was found that the constructed network inherits the multiscale properties of the time series in its structure. For example, periodic series and noisy series convert into regular networks and random networks, respectively. A novel method for the chaotic time series analysis is provided.

Chaotic Response and Bifurcation Analysis of Gear-Bearing System with and without Porous Effect under Nonlinear Suspension

Chaotic Response and Bifurcation Analysis of Gear-Bearing System with and without Porous Effect under Nonlinear Suspension

Chaotic Response and Bifurcation Analysis of Gear-Bearing System with and without Porous Effect under Nonlinear Suspension

Chaotic Response and Bifurcation Analysis of Gear-Bearing System with and without Porous Effect under Nonlinear Suspension

Experimental investigation on noise of cavitation nozzle and its chaotic behaviour

The researches of cavitation noise mainly focus on the incipiency and developing of cavitation to prevent the cavititation erosion in the hydraulic machinery, while there is few report about the collapse strength of cavitation bubbles produced by water jet through the cavitation nozzle to utilize efficiently the collapse energy of cavitation bubbles. The cavitation noise signals are collected with hydrophones for the cavitation nozzle and general nozzle at the target position and the nozzle exit separately in the conditions of different standoff distance. The features of signal’s frequency spectrum and power spectrum are analyzed for various nozzles by way of classical methods. Meanwhile, based on chaotic theory, phase space reconstruction is processed and the maximum Lyapunov index is calculated separately for each cavitation signal’s time series. The results of chaotic analysis are compared with the one of conventional analysis. The analyzed data show that there are the marked differences at the spectrum between the cavitation nozzle and general nozzle at the target position while the standoff distance is 35 mm, which mainly displays at the high frequency segment (60–120 kHz). The maximum Lyapunov index calculated appear at standoff distance 35 mm, which is an optimum standoff distance for the most bubbles to collapse at the target. At the nozzle exit, the noise signal of cavitation nozzle is different from the general nozzle, which also displays at the high frequency segment. The results demonstrate that the water jet modulated by the cavitation nozzle can produce effectually cavitation, and at the target position the amplitude and energy of noise spectrum in high frequency segment for cavitation nozzle are higher than conventional nozzle and the Lyapunov index of cavitation nozzle is larger than conventional nozzle as the standoff distance is less than 55 mm. The proposed research reveals that the cavitation noise produced by collapse of cavitation bubbles attributes mainly to the high frequency segment of the spectrum, which provides references for the research on cavitation noise.

Risk Warning of High-Tech Enterprise Independent Innovation Based on Chaotic Neural Network

Risk warning evaluation index system of independent innovation is established according to the process of innovation activities of high-tech enterprise, Chaotic Analysis Method is introduced into the BP(Back Propagation)Neural Network Model to research the early risk warning of high-tech enterprises independent innovation, the empirical results show that the integration of early warning model is feasible and effective, and significantly improve the convergence speed of network training, to some extent, avoid getting into local minimum.

Research on Ship Swaying Motion Prediction Based on Multi-Variable Chaotic Time Series Analysis

Wind and waves have particularly significant influence upon exertion of naval vessels battle effectiveness. It is urgently necessary to improve the ability of the Navy to carry out combat service in severe sea state normally. This paper aims to obtain the accurate prediction of ship motions with second level predictable time in real waves. According to the characteristics of the ship motion, the research on extremely short-time prediction of ship motion has been carried out based on multi-variable chaotic time series analysis, and the effectiveness of the prediction of ship motion in real wave is highly improved.

Chaotic Analysis of Reservoir Inflow Series: A Case Study on Koyna Reservoir Inflow

The significance of treating the reservoir inflow as a chaotic system instead of a stochastic system is gaining interest in the recent past. Out of various chaotic methods available to analyse time series data, correlation dimension method, the most commonly employed method in hydrology is used in the present study. Apart from identifying the behaviour of the series, correlation dimension method indicates the number of dimensions required to predict the future value in the time series. Correlation dimension of a time series is estimated using Heaviside step function through Grassberger–Procaccia algorithm. In order to prove the reliability of Grassberger–Procaccia algorithm, initially a deterministic series and two types of random number series are analyzed to identify their behaviour as well as to determine the correlation dimension. The daily reservoir inflow observed at Koyna reservoir for a period of 49 years (1961–2009) in Maharashtra, India, has been taken up to study its behaviour. From the detailed non-linear dynamic analysis using correlation dimension method it is found that the Koyna reservoir inflow is showing a low chaotic behaviour. The minimum correlation dimension for the daily Koyna reservoir inflow is around one and maximum correlation dimension is around four. It is also evident that only short term prediction is reasonable in this reservoir. This study proves the strength of Grassberger–Procaccia algorithm in classifying the behaviour of time series.

FBG/DFB-induced chaotic self-mixing analysis and sensing applications

Optical feedback induced by a highly reflective fiber Bragg grating (FBG) onto a distributed feedback (DFB) laser is investigated. In static strain conditions, the covariance matrix of the chaotic noise generated by DFB/FBG self-mixing interference is decomposed into its eigenvalues. Then, by observing the eigenvalue pattern, it is possible to detect the strain applied to FBG independently from source power and electrical noise level. Furthermore, by employing a Capon spectral estimator, and splitting and filtering out the self-mixing eigenvalues, it is possible to dynamically detect acoustic signals in harsh signal-to-noise conditions. This technique provides dual static and dynamic FBG interrogation independent on optical power and fiber link losses variations.

Fractional Order Generalization of a Three-scroll Chaotic Chua System and Dynamics Analysis and Simulation��

Fractional Order Generalization of a Three-scroll Chaotic Chua System and Dynamics Analysis and Simulation��

Chaotic dynamic behavior analysis and control for a financial risk system

According to the risk management process of financial markets, a financial risk dynamic system is constructed in this paper. Through analyzing the basic dynamic properties, we obtain the conditions for stability and bifurcation of the system based on Hopf bifurcation theory of nonlinear dynamic systems. In order to make the system's chaos disappear, we select the feedback gain matrix to design a class of chaotic controller. Numerical simulations are performed to reveal the change process of financial market risk. It is shown that, when the parameter of risk transmission rate changes, the system gradually comes into chaos from the asymptotically stable state through bifurcation. The controller can then control the chaos effectively.

Chaotic analysis of EMG on the patients of facial paralysis

Surface electromyography (sEMG) has been widely applied to disease diagnosis, pathologic analysis and rehabilitation evaluation. It is the nonlinear summation of the electrical activity of the motor units in a muscle and can reflect the state of neuromuscular function. Traditional linear and statistical analysis methods have some significant limitations due to the short-term stationary and lower signal-noise ratio of sEMG. In this paper, we introduce chaotic analysis into the field of sEMG process to investigate the hidden nonlinear characteristics of sEMG on the patients of facial paralysis. sEMG on the bilateral masseter, levator labii superioris and frontalis of the 21 patients is recorded. Chaotic analysis is employed to extract new features. A time delay is introduced to describe the signal in a multi-dimensional phase space; correlation dimension and Lyapunov exponent are obtained to verify the chaotic characters of EMG signals; and approximate entropy is calculated to quantify the amount of regularity and the unpredictability of EMG. Finally, significant differences can be seen according to the comparison between the aforementioned features on the healthy and diseased sides. Chaotic analysis can provide a new insight into the complexity of the EMG and may be a vital indicator of diagnosis and recovery assessment of facial paralysis.

Autonomic Dysfunction in Chronic Fatigue Syndrome

Symptoms of autonomic dysfunction are one of the characteristic features of the clinical condition known as chronic fatigue syndrome (CFS). In this study we examined the autonomic nerve functions in 1,099 patients with CFS and in 361 normal healthy controls. The autonomic nerve functions were studied in terms of heart rate variability and spectral analysis, and by using the maximal Lyapunov exponent (MLE) and chaotic analysis. The heart rate variability analysis revealed that both the logarithmic low-frequency power (Log LF) and logarithmic high-frequency power (Log HF) were reduced significantly with age in healthy subjects (p < 0.005). When we compared these parameters between the CFS patients and healthy controls according to age groups, there was no significant difference in Log LF between CFS and control except in the case of the 40's age group. However, Log HF decreased significantly in moderate and severe fatigue CFS groups in all age groups. All of the severe fatigue CFS age groups had a significant increase in the ratio of Log LF/HF as compared with that of the healthy controls. When we studied the change in these power values during sleep as compared with that during awake time, the HF power rose 3.03-fold in the controls and 1.86-fold in the CFS group during sleep as compared with that during the awake time; and this difference in fold increase during sleep between the CFS and control groups was significant (p < 0.05). Furthermore, the MLE for both the moderate and severe fatigue CFS groups was decreased significantly as compared with that of the healthy control group (p < 0.01). It is well known that autonomic nervous function differs greatly in individuals, but our present large research study indicates that there is no doubt that parasympathetic nervous dysfunction is involved in the pathophysiology of CFS patients with severe fatigue. We incorporated the evaluation of autonomic function into the Japanese CFS diagnostic criteria in 2012, because this evaluation might be a useful objective diagnostic tool for CFS.

Chaotic characteristic analysis for traffic flow series and DFPSOVF prediction model

Time delay and optimal embedding dimension for the real measurement traffic flow series, which are used by mutual information method and false nearest-neighbor method, respectively, are determined for phase space reconstruction of the traffic flow series. The saturation correlation dimension and the largest Lyapunov exponent for traffic flow series are calculated to estimate its chaotic characteristics. Based on the least mean square (LMS) algorithm, a novel second-order Volterra model using Davidon-Fletcher-Powell method (DFPSOVF) is constructed, in which a variable convergence factor based on a posteriori error assumption, characteristic of real-time change with the input signal, is applied. DFPSOVF model can avoid some problems caused by improper selection of parameters when using LMS adaptive algorithm for coefficient adjustment in Volterra model. DFPSOVF model can also be applied to short-term traffic flow prediction with chaotic characteristics. Results show that when model memory length is consistent with embedding dimension of traffic flow series, it obtains higher prediction accuracy, which can meet the needs for traffic guidance and traffic control, and can also provide a new method, a new idea and engineering application reference for intelligent traffic control.

Chaotic and Subharmonic Motion Analysis of Floating Ring Gas Bearing System by Hybrid Numerical Method

This paper studies the nonlinear dynamic behaviors including chaotic, subharmonic, and quasi-periodic motions of a rigid rotor supported by floating ring gas bearing (FRGB) system. A hybrid numerical method combining the differential transformation method and the finite difference method used to calculate pressure distribution of FRGB system and rotor orbits. The results obtained for the orbits of the rotor center are in good agreement with those obtained using the traditional finite difference approach. Moreover, the hybrid method avoids the numerical instability problem suffered by the finite difference scheme at low values of the rotor mass and computational time-step. Moreover, power spectra, Poincaré maps, bifurcation diagrams and Lyapunov exponents are applied to examine the nonlinear dynamic response of the FRGB system over representative ranges of the rotor mass and bearing number, respectively. The results presented summarize the changes which take place in the dynamic behavior of the FRGB system as the rotor mass and bearing number are increased and therefore provide a useful guideline for the bearing system.

Chaotic Response and Bifurcation Analysis of Rotor-Bearing System with Nonlinear Suspension and Roughness Effect under Turbulent Flow

This research aims to study a numerical analysis to investigate the dynamic orbits of a rotor supported by two turbulent journal bearings considering longitudinal and transverse roughness effect under nonlinear suspension. The dynamic response of the rotor center and bearing center are studied. The dynamic trajectories of the rotor center and bearing center, Poincaré maps, bifurcation diagrams and the maximum Lyapunov exponent method are employed to analyze dynamic responses in this study. The results show that either the longitudinal and transverse or the values of roughness parameters c1 would not affect strongly nonlinear rotor-bearing system very much but the difference of dynamic response between considering with roughness effect and without roughness effect are very obvious. The modeling results provide some theoretical references for further researching of rotor-bearing system for rotating machinery that operate in highly rotational speed, especially it considers roughness effect, turbulent flow and nonlinear suspension of bearing system.