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Objective To explore the chaotic dynamic mechanism of the formation and change of smoking behavior. Methods Based on the chaos theory, the chaotic characteristics of smoking-quitting behavior, the dynamic mechanism of chaotic process and chaos control strategy were analyzed by using typical analysis and comparative analysis. Results The formation and change of smoking habit accorded with the typical characteristics of chaos. The individual smoking behavior belief, which is composed of psychological cognition, behavioral motivation, and external environment/situation/occasion, is the aattractorn to induce and control the development, evolution, and disappearance of smoking behavior. The a scenario-motivationn coupling mechanism is the power source of smoking behavior, and multiple scenario-motivation, coupling mechanisms cooperate to dominate the behavior, which gradually increases the frequency of behavior and enters a chaotic state. The stability of chaotic system makes it difficult to quit the habit for a long time. Quitting smoking needs to fundamentally break the chaotic state of behavior and deconstruct multiple a scenario-motivation0 coupling mechanisms in chaotic attractors. Conclusion This study puts fonvard the chaotic dynamic mechanism of the formation and change of smoking habits, which provides a new perspective and train of thought for deeply understanding, explaining, and intervening other health-related behaviors and habits. It provides theoretical support for individual smoking cessation and smoking control policy-making. [ABSTRACT FROM AUTHOR]

For the problems of uneven initial population and premature convergence in the basic grey wolf algorithm, grey wolf optimization(GWO) algorithm is improved from three aspects based on chaos theory, and a chaotic grey wolf optimization(CGWO) is proposed for determining the minimum safety factor of the slope. Firstly, an improved Tent chaotic mapping is used to improve the initial population diversity; Secondly, a chaotic perturbation strategy is used to avoid the algorithm from falling into a local optimal; Finally, a parametric chaotic non-linear adjustment mechanism is introduced to balance the global exploitation and local exploration arithmetic of the algorithm. Simulation results of 13 benchmark test functions show that the improved algorithm has a stronger integrated optimization search performance compared with the basic GWO, WOA, PSO and SCA. Selecting the ACADS side slope assessment questions for computation and analysis, the CGWO algorithm shows a high computational accuracy and convergence speed, and can effectively search for the minimum safety factor of complex stratified slopes. Compared with the finite element strength reduction method, the method has the advantages of easy operation and easy setting of the search area. [ABSTRACT FROM AUTHOR]

For the problems of uneven initial population and premature convergence in the basic grey wolf algorithm， grey wolf optimization(GWO) algorithm is improved from three aspects based on chaos theory， and a chaotic grey wolf optimization(CGWO) is proposed for determining the minimum safety factor of the slope. Firstly， an improved Tent chaotic mapping is used to improve the initial population diversity; Secondly， a chaotic perturbation strategy is used to avoid the algorithm from falling into a local optimal; Finally， a parametric chaotic non-linear adjustment mechanism is introduced to balance the global exploitation and local exploration arithmetic of the algorithm. Simulation results of 13 benchmark test functions show that the improved algorithm has a stronger integrated optimization search performance compared with the basic GWO， WOA， PSO and SCA. Selecting the ACADS side slope assessment questions for computation and analysis， the CGWO algorithm shows a high computational accuracy and convergence speed， and can effectively search for the minimum safety factor of complex stratified slopes. Compared with the finite element strength reduction method， the method has the advantages of easy operation and easy setting of the search area. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of Harris eagle algorithm (HHO) in solving Job-Shop scheduling problem (JSP), such as poor optimization ability and easy to fall into local optimization, this paper proposed a hybrid Harris eagle algorithm (HHHO). Firstly, the algorithm used chaos theory to increase population diversity in the population initialization stage. Secondly, in the early stage of HHO search, the algorithm used nonlinear energy decline and quantum computing to enhance the global exploration ability of the algorithm. In the later stage of search, the algorithm adopted neighborhood search algorithm to enhance the local development ability of the algorithm. Finally, the algorithm selected FT and LA basic examples to test the performance of the algorithm, and compared with other advanced meta heuristic algorithms to verify the effectiveness and superiority of HHHO in solving JSP. [ABSTRACT FROM AUTHOR]

A new formation mechanism of the low-frequency broadband within gapped nonlinear local resonance structures was revealed based on the nonlinear chaos theory, and a novel concept for designing nonlinear local resonant structures with small gaps was further proposed. Due to the small gaps, the nonlinear chaos phenomenon occurs in the local resonance system, which can change the spectrum structure in vibration noise successfully, and the linear spectral energy greatly weakens and a continuous broad spectrum forms after chaotic motion, to effectively isolate the low-frequency spectrum. Most importantly, the finite element results show that, the nonlinearity of the small gap indeed leads to the low-frequency band-gap within the nonlinear local resonance. Therefore, the new idea for designing the nonlinear local resonance structure makes a new way to the development of local resonant elastic metamaterials, and the formation mechanism of low-frequency band-gap based on the nonlinear chaos theory lays a very important theoretical basis for vibration and noise reduction. [ABSTRACT FROM AUTHOR]

With the help of discontinuous mapping and tail mapping, the maximum Lyapunov exponent of the system with the above three structures was constructed using Khasminskii transform. For the shape memory alloy beam model with unilateral constraints, the corresponding Lyapunov exponent sequences were analyzed and the bifurcation and chaotic motions of the system were discussed. The results show that the validity of the maximum Lyapunov exponent algorithm is verified by combining the time history diagram and phase diagram. [ABSTRACT FROM AUTHOR]

Due to the existence of observation noise such as multi-path error, the accuracy of deformation prediction results are affected by using the data series of GNSS deformation monitoring. In order to examine the influence of measurement error on the deformation prediction results, the GNSS derived surface displacement time series of Miaodian landslide in Jingyang Area of Shaanxi, and those after noise suppression in combination with chaos theory were analyzed. Firstly, the mutual information method was used to determine the time delay of the surface displacement time series, and the Cao method was used to determine the embedding dimension to obtain the phase space reconstruction parameters. Secondly, the maximum Lyapunov exponent method was used to identify the chaotic characteristics of the two surface displacement time series. Finally, the weighted first-order local prediction method, the largest Lyapunov exponent prediction method, and the BP neural network prediction method were used to predict the landslide surface displacements. The results show that the GNSS landslide surface displacement time series and the time series after noise suppression have chaotic characteristics. The BP neural network prediction method has good prediction performance with an MAE of 0.4 mm and an MRE of 11.9%. After S-transform noise suppression, the MAE and MRE are 0.1 mm and 4.12%, respectively. Compared with the original time series, the prediction performance has been significantly improved after noise suppression. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a common chronic neurological disease. Because of its unclear etiology, the early diagnosis of is quite difficult. This article proposed a novel model based on improved optimized kernel extreme learning machine (KELM) for the early diagnosis of PD. This paper used chaos theory and Gauss mutation factor to improve salp swarm algorithm (SSA), and designed the intelligent diagnosis model named as ISSA-KELM based on evolution mechanism. The improved SSA (ISSA) used to conduct adjust KELM important parameters and feature subsets selection synchronously. Moreover, the model was processing on multi-thread scheduling on open MP platform, which could achieve the maximum classification accuracy of model. and further improved the computational efficiency. The experimental results show that the classification accuracy of the proposed model is higher than those of the existing methods, the computational efficiency is algo greatly enhanced, it demonstrated the good comprehensive performance. Therefore, it proves that the proposed model has goods prospects for application and it will be helpful for clinicians to make more accurate decision in diagnosis of PD. [ABSTRACT FROM AUTHOR]

To solve the problems of residual current protection device with refusing to operate, maloperation and difficult extracting of electric shock current signal, a chaotic detection method of electric shock current was proposed based on time domain waveform envelope characteristics of chaotic signal. Based on the sensitivity of chaotic system to initial conditions and immunity to noise, a phase detection method was proposed by vector analysis according to chaos theory. According to the difference of output x-time-domain waveform envelope in different states of chaotic system, the standard deviation of x-time-domain waveform extreme point sequence was proposed as index to measure the fluctuation characteristics of waveform envelope, and the critical state of chaotic system could be automatically identified. A method of detecting the amplitude of electric shock current was proposed by vector analysis. The results show that the proposed chaotic criterion can accurately judge the periodic state and chaotic state of the system, and the electric shock current signal can be detected from the total residual current with average error of 4. 64%. [ABSTRACT FROM AUTHOR]

Given the discontinuous structure of vibro-impact system. Newton iteration was used to establish an effective numerical integration strategy. and this numerical integration strategy was applied to the cell mapping algorithm. The effectiveness of this method was verified by an application example of a typical duffing vibro-impact system. and the global coexistence attractor and global catastrophe of the system were further discussed. The research shows that Newton iterative integration method is suitable for vibro-impact system, and the study of periodic motion and chaotic motion of the system is not only effective, but also efficient. The algorithm can quickly lo- cate the collision time and improve the calculation speed. With the change of parameters. there are abundant catastrophe phenomena in the vibro-impact system. including the rout from period directly to chaos. the crisis of multi-periodic solution and chaotic solution. [ABSTRACT FROM AUTHOR]

A chaotic parameter-optimized radial basis function (RBF) forecasting model was proposed. The chaos theory was used to obtain the embedded dimension and delay time of the ENPEMF， and the obtained parameters were used to optimize the RBF neural network. Finally， the trained optimized-RBF was utilized to forecast the strength trend of 14 d ENPEMF data. Numerical simulation results show that the improved RBF model could forecast the Rossler time series well with small error. Applying the improved RBF algorithm to the ENPEMF data before Ms7.0 earthquake in Lushan， it can effectively forecast the ENPEMF intensity trend 14 d before earthquake. The forecasting effect and accuracy are significantly better than that of the traditional RBF algorithm， which is expected to provide support for electromagnetic monitoring and analysis before earthquakes and geological disasters. [ABSTRACT FROM AUTHOR]

Aiming at the problem of single structure style and less system variable for traditional chaotic system, this paper proposed a chaotic circuit, which had more complex dynamic behaviors, high er sensitivity to variable and stronger anti-interference. With the change of circuit parameters, this pa per discussed the different dynamic behaviors of the system in detail based on the adaptive stability criterion and chaos motion theory. In addition, it constructed the corresponding circuit and observed the system dynamics phenomena by oscilloscope. The dynamics phenomena were in accordance with the simulation results of MATLAB, which further proved the feasibility and flexibility of the circuit. On this basis, it put forward and studied the multi-stage combination synchronization based on this system. By constructing different controllers, this paper realized the multi-stage combination synchronization among the eight systems. The simulation results show th at the synchronization scheme has a good effect on convergence speed and precision. [ABSTRACT FROM AUTHOR]

Some Duffing systems with external excitation and quintic nonlinear restoring forces were considered, the amplitude-frequency response equation for the system was obtained with the multi-scale method, and the amplitude-frequency characteristic curves and their changing rules under different parameter changes were given. At the same time, the singularity theory was applied to get the transition sets and the corresponding topological structures of I he system in 3 cases. Second, the fixed point of the system was determined, and the Hamiltonian function was used to get the heteroclinic orbit of the system, so the threshold of chaos in the Smale horseshoe sense was obtained with the Melnikov method. Then, the dynamic bifurcation and chaotic behavior of the system under external excitation and quintic nonlinear coefficients were given through numerical simulation. It is found that there are nonlinear phenomena such as periodic motion, period doubling motion, quasi periodic motion and chaos. The correctness of the theory was verified with nonlinear methods such as the Lyapunov exponent, the phase diagram and the Poincaré sections. The work provides a theoretical reference for further understanding of the nonlinear characteristics of Duffing systems and their evolution laws. [ABSTRACT FROM AUTHOR]

An optimization method for spatial configuration of aircraft swarm based on self-adaptive Tent chaos search grey wolf optimizer is proposed for the optimal spatial configuration of aircraft swarm in the coordinated attack of fighters and unmanned aerial vehicles. A mathematical model of cooperative attack zone of air-to-air missiles is established, and a calculating method for cooperative attack zone under the condition of any aircraft swarm spatial configuration is proposed. The self-adaptive Tent chaos search is applied in grey wolf optimizer to improve the performance of the algorithm, thus avoiding the local optimum and the slow search speed. The size of cooperative attack zone is taken as the target function, and the spatial configuration of aircraft swarm is optimized by using the improved algorithm. The simulated results show that the improved algorithm can be used quickly to obtain the optimal spatial configuration with the largest cooperative attack zone and the minimal threat, and the cooperative attack zone of aircraft swarm is significantly larger than the sum of single aircraft attack zone under the optimal spatial configuration. [ABSTRACT FROM AUTHOR]

The modeling and synchronization control experiments of the Lorenz chaotic system are carried out by using Matlab/Simulink software. According to the differential equations of the Lorenz system, four methods are used to model and simulate respectively, and these methods are analyzed. By using the state linear feedback control, the synchronous control experiment of the Lorenz chaotic system is designed to realize the synchronization control of the two Lorenz chaotic systems. The simulation experiment can deepen the students’ understanding of the chaotic system modeling and synchronization control, and provide a new way for chaos theory and experimental teaching. [ABSTRACT FROM AUTHOR]

Compressive sensing is a new sampling theory which makes use of the sparsity of signals to recover the high-dimension signal from very few measurements. Unfortunately,most of the existing measurement matrices have high correlation between elements,which can not guarantee exact recovery and has greatly restricted their application prospects. Based on this,this paper proposed a novel Chebyshev chaotic measurement sensing matrix based on sampling columnization( SC3M). The sampling columnization and normalization guaranteed the low correlation in column of SC3 M,which optimized the recovery effect. Moreover,it proved that the proposed SC3 M sensing matrix satisfies the RIP with overwhelming probability from the Johnson-Lindenstrauss lemma. Numerical simulations show that the SC3 M is sufficient to guarantee exact recovery,which shows better effect compared to other popular measurement matrixes such as the fully random matrix,Bernoulli matrix and Gaussian sensing matrix. [ABSTRACT FROM AUTHOR]

A hybrid adaptive genetic algorithm is proposed for the task scheduling of phased array radar. An optimal scheduling model for phased array radar is established. The performance and efficiency of the algorithm are improved by optimizing the initial population by the chaos theory, adopting the selection strategy of elite reservation and mixed ranking, and designing the adaptive crossover and mutation operators. A heuristic pulse interleaving algorithm is presented based on the adaptive genetic algorithm. It could utilize the waiting period in a task to execute the transmitting period or receiving period of other task. The simulated results demonstrate that the proposed algorithm provides better results and search solutions more quickly than the genetic algorithm. Moreover, compared with the heuristic scheduling algorithm , the proposed algorithm improves the scheduling success ratio, time utilization ratio and high value ratio, and decreases the average time shift ratio efficiently. [ABSTRACT FROM AUTHOR]

There are advantages in analysis of the intrinsic chaotic component in the fitting residuals in displacement monitoring statistics as well as in traditional algorithms for mining dam monitoring information. This paper therefore combines the characteristics of the conventional optimization algorithm, based on using frog leaping algorithm (SFLA) to determine the optimum weight in the sub-model, to establish a dam displacement combination monitoring model based on SFLA. Taking the chaotic characteristics of fit residuals in the statistical analysis into account through using phase space reconstruction and chaos theory,we analyzed displacement residuals and predicted values, and superimposed the forecast residual term with SFLA model predictions and developed leapfrog algorithm combination forecasting methods fusing chaos residuals, and a dam displacement leapfrog algorithm implementation process that considers of the chaotic residuals. Examples show that the forecasting ability of this model provides a new, improved approach to the analysis of dam deformation. [ABSTRACT FROM AUTHOR]

In metal cutting process, surface quality and dimensional accuracy of the work piece is affected by cutting-tool wear condition. So it is important to study the cutting-tool wear, especially in automation production. Cutting-tool wear is a complex process; it is affected by various factors like cutting parameters, material characteristics and cutting environment, etc. Metal cutting is a nonlinear system; there are a lot of non-stationary signals used in condition monitoring and fault diagnosis. Vibration, force and acoustic emission (AE) are the typical signal type widely used in cutting-tool wear research. In this paper, we chose AE signal to be the carrier in analyzing cutting-tool wear. AE is the class of phenomena where transient elastic waves are generated by the rapid release of energy when the materials are distorted or under the outside load. The AE signal produced by cutting -tool wear is high-frequency and the bandwidth is nearly 50 kHz-1 MHz, so it can weaken the influence of low-frequency noise like mechanical noise and ambient noise. The measured signal sometimes contains high-frequency noise. In this paper, chaos theory was used in analyzing the nonlinear characteristics of the AE signal. Chaos theory is sensitive to noise; therefore, noise reduction was done with the method based on empirical mode decomposition and wavelet (EMD-Wavelet) before computing. The signal were decomposed into several intrinsic mode functions which was from high-frequency to low-frequency by use of EMD, then it was used to determine the noise dominated intrinsic mode functions based on consecutive mean square error (CMSE) proposed by Boudraa and then restrained them. A new signal were reconstructed by adding the rest intrinsic mode functions together and a further and last de-noising was using wavelet to processing the new one in order to get more pure signal. Before extracting the chaotic character, an important step was to reconstruct a phase space from the de-noised signal. To get the phase space vector, two key parameters, delay time and embedding dimension, had to be determined. Method based on mutual-information was utilized in computing delay time and Cao method for embedding dimension. After reconstructing the phase space, the chaos attractor was presented which can obviously reflect the cutting-tool wear condition. The structure of the attractor differed with tool wear. In order to prove the effect of noise reduction, a comparison was done between attractors another one reconstructed from original signal. The attractor reconstructed from the purified signal was smoother than the noisy signal. To get accurate result, the correlation dimension was computed. The result showed that seemingly random AE signal has internal ordered state and there was a certain correspondence between the correlation dimension and tool wear. So the chaos character can be used in cutting-tool wear identification and the result can offer a reference for cutting-tool wear condition monitoring. [ABSTRACT FROM AUTHOR]

In order to extract the features of fault signals of dynamic system polluted and modulated by background noise, and solve anomaly detection problem of slight fault based on the generated normal patterns, this paper considers the one-class classification problem of insufficient fault samples and class imbalanced in intelligent monitoring and diagnosis for dynamic systems. Besides, it is well known that the conventional feature selection methods always depend on specific fault types. If some features are chosen to identify one fault, their performance may be poor for other fault cases. Compared with other classifiers, support vector data description (SVDD) is only based on samples at normal state, and its optimization hyperplane is independent of specific fault types. Chaos and fractal are the nonlinear phenomena which widely exist in nature and socio-economic system. Chaos and fractal can help to find out the internal regularity of system, and it is a new branch and the important fields of nonlinear science, whose subjects include unsmoothed, nondifferentiable geometry and long-range correlated structure in nonlinear system, method to judge if one system has chaos and fractal phenomena that mainly uses the largest Lyapunov index and fractal dimension to judge. Study has showed that the chaos and fractal theory can reflect the essence of the dynamic systems as complex system. Hence, a new abnormal identification and feature selection method of dynamic systems that combines chaos fractal feature and SVDD is presented. In this method, the receiver operating characteristic (ROC) curve for 2 classifying errors i.e. misdiagnosis and missed diagnosis in classification field based on SVDD is synthesized in the proposed method to make the selected chaos fractal and features more effective. The best combination of the largest Lyapunov exponent and correlation dimension is selected through analyzing chaos and fractal features, which include: the largest Lyapunov exponent, box dimension, correlation dimension and the mean of generalized dimension. To ensure good performance in the environment of complex dynamic systems, anti-noise robustness analysis is conducted. The data samples of different normal states are trained to obtain the optimal enclosing spheres respectively. And the relative distances between diagnostic samples and the distributed spheres are introduced to decide which class they belongs to. In this way SVDD classifier of normal condition samples has been proposed based on combined features as the input of SVDD classifier. In addition, SVDD is a robust data domain description method; its performance, however, is strongly influenced by kernel parameter. To improve the low fraction of target class that is accepted by the SVDD in the case of atypical target training data, experimental studies for seeking optimum parameters of kernel functions which are used to improve classification precision have been discussed. The penalty coefficient B is fixed to the value of 0.4, and in this case, the optimal kernel parameter value is suggested as a value of 0.6, which can prevent effectively the phenomenon of over-fitting and has good fault sensitivity and generalization. Abnormal identification model is set up based on the normal history data of the system to detect the abnormal condition by comparing the projection on the chaos fractal feature space to which the real measurement data and normal data were projected. Using SVDD-ROC as feature extraction method, the problem that traditional feature extraction methods depend on specific fault samples is avoided. The experiments with single-point fault data simulated on bearing vibration test-bed have been implemented to evaluate the efficiency and performance of the clustering, and results of test show that the proposed method only requires the samples at normal state to identify condition between normal states and fault states, and it is able to well distinguish unknown fault types. The study can provide theoretical and design basis for the modeling and detecting of dynamic systems with fault conditions, which is helpful to prevent incident, save maintenance cost, improve the utilization rate of the system, protect the running of production, and thus reduce costs. [ABSTRACT FROM AUTHOR]

In order to investigate the evaluation of neuronal firing time series with time, this paper proposed an improved visibility graph method called local visibility graph method for constructing complex networks from time series, which could map time-domain of neuronal firing time series into network domain. By constructing a complex network topology and calculating its statistical properties in a sliding time window, the method described the time-varying statistical characteristics of neuronal firing time series. As a paradigm, three time series of different bursting modes generated by map neuronal model were used to construct networks, realize visualize topology and analyze statistical characteristics. The results show that network topology, average path length and clustering coefficient can depict time-varying morphological characteristics of the time series. The three properties show parameter sensitivity to neuronal bursting. There is a correlation between sparse degree of neuronal bursting and the community size in the complex networks. Evolution characteristics of neuronal firing time series in network-domain can depict their time-domain features, and the ideology proposed in this paper provides a new way for on-line neural signal processing. [ABSTRACT FROM AUTHOR]

Specific to the security issues of medical information falsifying and stealing, this paper proposed a zero-bit water mark algorithm based on chaos. It used the Arnold algorithm to scramble the original carrier image for k times. Then it extracted the least significant bit of each pixel of the scrambled image for the XOR operation with binary watermark to construct the zero-bit watermark. The actual embedded watermark image adopted chaos mapping encryption to hide the watermark information. After it extracted the watermark, took the absolute difference between the original watermark image and the extracted one, if it had suffered from falsifying, the falsifying position and shape could be located. The experimental results show that the large key space of chaotic system and sensitivity to the initial value can increase the robustness of the algorithm, and the algorithm can still accurately locate the distorted site and shape under conventional attacks. [ABSTRACT FROM AUTHOR]

Considering these factors, such as working time and rush hours can affect the speed of vehicles, this paper established collaborative vehicle routing problem (CVRP) mathmatical model. Aiming at the shortcomings of ant colony optimization (ACO), combined the advantage of rapid global search ability of genetic algorithm(GA), and changed the pheromone volatilization factor adaptively, moreover, initial population generated by chaotic search could accelerate chromosome convergence to the optimal solution, a smooth mechanism helped to search for more effective search space, thus it constituted a hybrid adaptive ant colony optimization (HAACO). And then it adopted ACO and HAACO to solve CVRP model. Experiments show that HAACO is better than ACO in convergence speed and optimal results. [ABSTRACT FROM AUTHOR]

Aiming at the security problem of chaos theory in cryptography application, a chaotic block encryption algorithm was designed based on the theory of discrete coupled map lattice and asymptotic deterministic randomness. The algorithm used aymptotic deterministic randomness key to diffuse the plaintext, and through the iteration of the discrete coupled map lattice the plaintext was encrypted. Then the validity, sensitivity and resistance of algorithm were tested and analyzed. The results showed that the security of chaotic encryption algorithm was higher. [ABSTRACT FROM AUTHOR]

Lad-balancing is an important topic in the research on cloud computing. As the load-balancing in cloud computing is inefficient, with poor accuracy and dynamic changes in resource demand, this paper put forward a load-balancing model in the environment of cloud computing. It improved the optimal selection of target nodes and the accuracy of task load transferred by introducing the chaotic algorithm in the send policy and firefly algorithm in the recipient policy. The simulation experiment shows that the improved resource load algorithm can effectively avoid the imbalance in load processing and improve the overall processing capacity of the system. [ABSTRACT FROM AUTHOR]

According to the fact that current condition evaluation models lay more emphasis on the static observations of characteristic parameters and less on trend information inherent in sequential observations, a new condition evaluation model with static and dynamic evaluations is proposed. For the weighting of multi-dimensional characteristic parameters, a mutative scale chaos algorithm is applied to achieve optimal objective weighting, and a static condition evaluation model is established. Based on static evaluation, a "distance" which measures real-time deteriorating speed and standard deteriorating speed is opted to modify the static evaluation result. NSD-WKFCM (neighbor sample density weighted kernel fuzzy C-means) clustering algorithm is used to solve standard vector of deteriorating speed. A dynamic adjusting function based on "distance" combined with its parameter optimization algorithm is proposed, and the complete dynamic condition evaluation model is established. A case study is performed to verify the effectiveness of the model. [ABSTRACT FROM AUTHOR]

This paper presented a relational database watermarking algorithm based on PSO-BF by adopting swarm intelligence optimization algorithm for reference. The application of chaotic mapping resulted in watermark. The use of one-way hash function secured order of the tuples. It formulated the watermarking of relational databases as a constrained optimization problem, and used PSO-BF algorithm to solve the optimization problem and to handle the constraints. It decoded watermark bits by using threshold-evaluator and majority-voter, then decrypted them to derive the actual watermark sequence by utilizing chaotic mapping. The experimental results show that this algorithm has good robustness to the attacks of tuple deletion, alteration and insertion, and possesses good invisibility. [ABSTRACT FROM AUTHOR]

To obtain better evenness, this paper presented an improved algorithm of producing the chaotic pseudorandom sequence based on the analysis of nonlinear dynamic characteristics statistical characteristics of the one-dimensional Logistic map. The arcsine and arccosine transformation were used to enhance the randomness of pseudorandom sequence produced by the one-dimensional Logistic map. The qualitative analysis and the numerical calculation results show that the iterative times has little influence on the statistical characteristics of the improved Logistic map sequence and the better evenness can be obtained with regard to the previous works. [ABSTRACT FROM AUTHOR]

The chaotic dynamics in a commensurate fractional-order nonlinear financial system is studied from the time-domain point of view. Through obtaining and analyzing all equilibrium points of the financial system, the necessary condition for the existence of chaotic attractors in the system is derived via the stability theory of fractional-order system. It has been found that chaos is yielded with lowest order 2.55 in the fractional-order financial system. Meanwhile, a reliable binary discriminant for chaos (called "0-1" test) is utilized to detect the chaos presence in the system dynamics. The validation of theory analysis and the effectiveness of the proposed method are demonstrated by the simulation results. [ABSTRACT FROM AUTHOR]

This paper presented an image encryption algorithm based on hyperchaotic sequences and Feistel structure. Firstly, Kawakami mapping generated pretreatment chaotic encryption sequences to preprocess original image. Secondly, Logistic mapping generated initial values of Hyperhenon mapping. Hyperhenon mapping produced encryption sequences as S-box of Feistel structure. Thirdly, the proposed algorithm utilized Feistel structure to encrypt the pretreatment image. The experimental results show that the algorithm has a higher stability, security, and encryption efficiency. [ABSTRACT FROM AUTHOR]

Determination of the threshold value is the key to chaos-based weak signal detection. The precision of the threshold value invariably defined the precision of the detection. Currently, determining the threshold value by observing the phase space trajectory could hardly satisfy the demand for an accurate threshold value in a short time, while the Lyapunov index method was complicated and it took a lot of time and computing resources, which was not convenient to program on the GPU platform. Comparably, the detecting method based on zero-crossing number could achieve an accurate threshold value with lower computing resources and complexity, which could well applied to the modern high performance programmable GPU. By using the zero-crossing number criterion, this paper proposed a new parallel algorithm of detecting the accurate threshold value based on GPU platform. Experiments show that the parallel algorithm based on GPU can accelerate the determination of the threshold value up to 90 times compared with the single-thread algorithm at the amplitude step length of 10. This parallel algorithm brings light to the quick detection of the threshold value of different chaos detecting system. [ABSTRACT FROM AUTHOR]

To the multi-parameter optimization problem of SVM with mixtures kernels, this paper proposed an improved chaotic particle swarm optimization (ICPSO) to find the best combination of the basic parameters (penalty parameter, kernel parameter et.) and mixtures adjustable kernels parameter of SVM. The experimental results further demonstrate that the proposed method can effectively search the best parameters combination, and has better generalization ability and better regression effect. This method has good modeling and higher prediction accuracy for coal and gas outbursts. [ABSTRACT FROM AUTHOR]

In order to deepen understanding the mechanism of rockburst induced by dynamic loading, the judgement formula of failure, catatrophic displacement and the total released energy of compound coal-rock under coupled static-dynamic loading were obtained by time dependent damage model and catastrophe theory. And, nonlinear dynamic model of compound coal-rock under coupled static-dynamic loading was established. Based on this, author found that the interaction of loading energy and system inheret energy lead that the model evolution exhibits stepwise and chaotic characteristic. When the roles of the nonlinear system itself and of the external loads are the same, the system begin to enter the chaotic evolution stage. It follows that the interaction between the mine coal-rock structure and the coupled static and dynamic loadings is the key of the evolution of dynamic rockburst. At last, the experiments of coupled dynamic and static loadings vertified the theoretical results, and it shows that the theoretical analysis are in good agreement with the experimental values. [ABSTRACT FROM AUTHOR]

In order to overcome the shortcomings of feedforward neural network's slow convergence, involving the local optimal and depending on the initial weights, this paper proposed a new method to train feedforward neural networks (FNNs) parameters based on the iterative chaotic map with infinite collapses particle swarm optimization (ICMICPSO) algorithm. This algorithm made full use of the information of BP's error back propagation and gradient. It used ICMICPS as the global optimizer to adjust the neural networks' weights and thresholds, when network parameters converge around global optimum. And it used gradient information as a local optimizer to accelerate the modification at a local scale. Compared with other algorithms, results show that the performance of the ICMICPSO-BPNN method is superior to the contrast methods in training and generalization ability. [ABSTRACT FROM AUTHOR]

To improve the efficiency of feature selection, a feature selection method based on chaos search (CSFS) is proposed. Firstly, a mapping model for feature candidates and chaotic variables is established, which maps the feature candidates to the chaos space and realizes the interconversion between them. Secondly, the feature selection is carried out by means of the evolution of the chaotic variable. Finally, the classifier is used to evaluate the obtained feature vector. The features of weld defects are taken for example to verify the proposed method, which is compared with a gene-algorithm-based feature selection ( GAFS) method. The experimental results demonstrate that the computation time of CSFS is only 61. 1% of that by GAFS in the case of obtaining the feature vectors with the same recognition performance. [ABSTRACT FROM AUTHOR]

In order to solve the poor robustness of software watermarking and the low execution efficiency of watermarking sharing algorithm, this paper proposed a sharing software watermarking scheme based on chaotic optimization. The scheme took advantage of chaos system, matrix partition and chaotic scrambling the watermarking information to form sharing watermarking. When watermarking was embedded, using DPPCT topology graph encode the sharing watermarking, then putting the hash value of the watermarking information into the info field of each DPPCT. After the watermarking embedded, using chaotic encryption to protect all code and prevent reverse engineering and other methods to attack the software watermarking. Theoretical analysis and experimental results show that the scheme can effectively resist various semantics preserving transformation attacks, decrease the program load and improve robustness and execution efficiency of the watermarking. [ABSTRACT FROM AUTHOR]

In order to improve the security of secret image transmission, this paper put forward a kind of image encryption scheme based on hyper-chaotic sequences. Firstly, it added plaintext pixel information to the transformation of each element of hyper-chaotic sequences which resulted in both key sensitivity and plaintext sensitivity. Then, it shuffled and encrypted the plaintext pixels according to the separation scrambling key sequences and the gray replacement key sequences pixel by pixel. The simulation results show that the gray distribution of encrypted image is balanced with its cipher-text sensitive to plain-text and key, and it is secure with strong resistibility to various attacks, such as statistical, exhaustive and differential attacks, verifying the feasibility of this scheme. [ABSTRACT FROM AUTHOR]

The heating steam is an important secondary energy, so it is of great significance to predict the required steam load in the future hours, which is important for the thermal power plant to provide users with high quality heat load securely and economically. Steam load time series proves to be with chaos characteristics. According to Takens theorem, delay time and embedding dimension are calculated respectively using C-C method and Cao method, and the steam load time series is reconstructed in phase space, and then the steam load forecasting model is established using least squares support vector machine (LSSVM). A SA_WPSO algorithm (improved particle swarm optimization (PSO) with simulated annealing algorithm (SA)) is proposed to implement the optimization of LSSVM parameters. The simulation results show that the method can achieve good prediction results. [ABSTRACT FROM AUTHOR]

A chaotic time series hybrid forecasting method based on wavelet transform, least squares support vector machine (LSSVM) optimized by particle swarrn optimization (PSO) and generalized au-toregressive conditional heteroscedasticity (GARCH) is proposed. Firstly, the chaotic time series is decomposed and reconstructed into approximate series and detailed series. Secondly, the approximate series future values are predicted by PSO-LSSVM model; while the detailed series future values are forecasted by GARCH model. Finally, the sum of the approximate and detailed series future values is used as the final forecasting values. Two chaotic time series, namely, Mackey-Glass and variable-parameter Logistic, are used to evaluate the performance. The results imply that the proposed method can provide more accurate results, which demonstrates the validity of this method. [ABSTRACT FROM AUTHOR]

The parameter estimation of chaotic system is a premise of system control and synchronization. In view of chaotic system's characteristics, such as sensitivity to initial condition, long-term unpredictability and so on, a filter applying to chaotic system was proposed based on chaotic system state space theory and particle filter (PF) theory. In a superimposed noise conditions, the parameter estimation and filtering of Lorenz chaotic system were simulated and analyzed. The simulation results show the proposed filtering algorithm is better than a chaotic system parameter estimation and filtering method based on extended Kalman filter (EKF) in bias estimates, and is an effective method for estimating the parameters of chaotic system and filter. [ABSTRACT FROM AUTHOR]

In order to reduce amount of computation, speed up convergence and restrain premature phenomena of quantum evolutionary algorithm, a hybrid chaotic quantum evolutionary algorithm is presented. The algorithm uses the chaotic initialization method to generate initial population that have better diversity, the simple quantum rotation gate to update non-optimal individuals of population to reduce amount of computation, and the hybrid chaotic search strategy to speed up its convergence and enhance its global search ability. A large number of tests show that the proposed algorithm has higher convergence speed and better optimizing ability than quantum evolutionary algorithm, real-coded quantum evolutionary algorithm and hybrid quantum genetic algorithm. Tests also show that when chaos is introduced to quantum evolutionary algorithm, the hybrid chaotic search strategy is superior to the carrier chaotic strategy, and has better comprehensive performance than the chaotic mutation strategy in most of cases. The proposed algorithm is the only one all of whose tests are convergent, and is easily implemented and applied in practice. It is satisfied in solving traffic signal timing optimization problem of urban road. [ABSTRACT FROM AUTHOR]

The problem of impulsive synchronous control of unified chaotic systems with parameter uncertainty and variable delays is studied. On the case that the master system's delay is different from that of the slave system, sufficient conditions for their uniform asymptotical synchronization are developed by employing the Lyapunov stability theory and matrix inquality techniques, and the impulsive controllers with respect to delays and initial value of systems are designed. Finally, the validity of the obtained results is shown by a numerical example and its simulation. [ABSTRACT FROM AUTHOR]

In order to discuss the effectiveness of the pretreatment method in literature 1, this paper adopted threshold and the way used the change T to quantify the real chaotic sequences which generated before and after pretreatment into binary sequences, respectively. Finally, it detected the 20 000 binary number sequences generated after quantization by the standard of FIPS 140-2 tests. The results indicate that the chaos sequences pretreated have good statistical properties. The randomness of 0-1 sequences after pretreatment with threshold quantization has very obviously improved by analysis of two quantitative methods. The randomness of 0-1 sequences use the change T is often better than the ones use threshold quantization whether using pre treated or not. [ABSTRACT FROM AUTHOR]

In order to improve identification exactness of chaotic system with noise, a new identification method based on wavelet neural network was presented, which was integrated with dynamic threshold and stochastic gradient. The method made dynamic threshold to affect wavelet coefficients, the train process was closely integrated with neural network and the threshold was dynamically and inversely modified based on error function and stochastic gradient, so the error was increasingly shrinking and approaching to the ideal output. The result showed that the method could reduce the noise of chaotic system and finally acquire better exactness. [ABSTRACT FROM AUTHOR]

Based on the semi-group property of Chebyshev chaotic map and RSA algorithm, this paper proposed a new chaotic maps-based key agreement protocol with hybrid security. The scheme utilized the RSA algorithm to protect the Chebyshev polynomial and thus resisted Bergamo attack. Theory analysis shows that the protocol has not only provided strong security, but also achieved high efficiency. [ABSTRACT FROM AUTHOR]

In order to improve the data coding rate and transmission security, according to the inverse function of iteration skew tent map, which could be used as arithmetic coding, this paper propsed a compression and encryption scheme based on the piecewise linear chaotic map. It solved the separate problems of encoding and encryption in the existing program. The simulation results indicate that the algorithm improves the coding efficiency and unpredictability of ciphertext. At the same time, it can effectively resist the attack of selective plaintext, short plaintext attack and brute force search attack. [ABSTRACT FROM AUTHOR]

To improve the security of network coding, this paper presented a secure network coding algorithm. It combined chaotic sequence with source information and used list-decoding to sink. This algorithm adopted advanced Logistic map to generate chaotic sequence in order to deal with the source information and the transmission process still kept the original network coding system. It not only can resist a variety of wiretapping attacks but also can resist pollution attacks. The system simulation and theoretical analysis confirm that this algorithm abandons part of bandwidth to ensure the security of network and achieves the information-theoretic security condition with minimum overhead. It finally realized the application of chaotic sequence in the secure network coding algorithm. [ABSTRACT FROM AUTHOR]

By combining Chebyshev polynomial with elliptic curve, this paper constructed an elliptic curve cryptosystem algorithm based on Chebyshev polynomial, and used the good semi-groop property of the limited domain Chebyshev and the properties of the elliptic curve. It realized cryptosystem algorithm in the elliptic curve. The algorithm has the advantages of the chaotic cipher and elliptic curve cryptographic algorithm. Through the analysis of the algorithm, it is thought that the algorithm is simple and has high safety, the scheme is feasible. [ABSTRACT FROM AUTHOR]

Based on the Si' lnikov theorem, this paper proposed a new three-dimensional square chaotic system, which had tow equilibrium poits. There was hyperbolic saddle focus. The formation mechanism showed that this chaotic system had Smale horseshoes (homoclinic chaos). Under satisfying the homoclinic Si' lnikov theorem, the large number of parameter values, which could be found, made that the system was chaotic state. Numerical simulation results show the effectiveness of the theoretical results. Finally, Smale horseshoses had been found in the system using undetermined-coefficient method, and it was chaotic in the sense of Si' lnikov. [ABSTRACT FROM AUTHOR]