105 results about "Chaos theory" patented technology

The software, methods and system of the current invention creates an interactive, auto-execution financial trading platform with unique forecasting algorithms, trading graphs and data mining features. The platform uses a univariate and multivariate architecture that is designed to improve performance of predictors and speed up calculations. Trading graphs, data mining features and predictive algorithms are predominantly based on fractal mathematics and Chaos theory. Even the unique software architecture is fractal in nature. All of these features are intended to be used individually or collectively to improve forecasting performance of financial markets. The above mentioned components also make it easier to manage a portfolio of securities and/or futures.

The invention discloses an improved fuzzy neural network bus intelligent scheduling method based on a chaos theory, and belongs to the field of intelligent transportation. According to the improved particle swarm bus intelligent scheduling method based on the chaos theory, advantages and complementarity of various algorithms are fully utilized, a series of improvement measures are also introduced, such as conjugate gradient optimization, and inertia factor and constraint factor of the particle swarm algorithm etc., the mechanism and the search performance are researched from the theoretical and practical perspectives, problems of poor global search capability and premature convergence of the conventional optimization algorithm are fundamentally solved, the diversity of population can be obviously increased, the global search capability is obviously improved, the problem of fuzzy information can be effectively dealt with, the convergence speed is fast, and a new high-efficiency method is provided for bus intelligent scheduling.

The invention discloses a method for realizing state monitoring and fault diagnosis of a water turbine based on a chaos theory, in particular, relates to a method for operating state monitoring and fault diagnosis of the water turbine, can preferably master the characteristic of instability of an internal flowing field of the water turbine, and solves the problem of difficult diagnosis of cavitation fault of a traditional water turbine. The method uses the promotion of wavelet transform for denoising pressure pulsation signals, and uses a chaos dynamics method for analyzing chaos dynamics characteristics of the water turbine in off-design operation so as to judge the caviation degree in the operation of the water turbine and to predict possible faults. The method is applied to the operation state monitoring and the fault diagnosis of the water turbine.

The invention relates to the technical field of network security evaluation, in particular to a network security situation prediction method based on a combination of the chaos theory and a neural network. The method comprises the following steps: carrying out processing of normalized network security situation value sequence sets through the mutual information method and the cao method to obtain the optimum embedded dimensions of network security situation sample values, carrying out phase-space reconstruction, and analyzing the maximum Lyapunov exponent of reconstructed samples to determine whether the evaluated samples have chaos predictability or not; determining the numbers of nodes of an output layer and a hidden layer of a BPNN according to characteristics of a nonlinear time sequence and experience; carrying out parameter optimization through an improved firefly algorithm, so as to determine network weights and offset values and establish a network security situation prediction model; and inputting test set samples into the BP neutral network for prediction, and carrying out denormalization of obtained prediction values. The method provided by the invention has the advantages that a network security situation can be more precisely predicted, and the network security situation prediction convergence rate can be increased.

The invention relates to a steam load prediction method. The steam load prediction method includes the steps: S1, collecting historical steam load data to form a steam load time sequence, conducting noise reduction on the steam load time sequence; S2, obtaining the embedded dimension and the delay time of the time sequence by means of the chaos theory, conducting phase-space reconstruction on the time sequence on which noise reduction is conducted, obtaining the sample data of a phase spatial domain to the sample data of the phase spatial domain before the reconstruction, obtaining a prediction model by means of a least square support vector machine; optimizing training parameters of the least squares support vector machine through an SA_PSO algorithm; predicting the steam loading value of the future 24 hours by means of the obtained training model, and obtaining and evaluating the obtained data. By means of the steam load prediction method, a satisfying result can be obtained under the condition that the external factors such as the data type and weather are not considered, and thus the steam load prediction method is simple and effective.

The invention relates to an electric energy quality steady-state index prediction method based on a chaos theory. The method comprises the following steps that: firstly, collecting electric energy quality steady-state index data; preprocessing collected electric energy quality steady-state index historical monitoring data, and storing processed data in a database after data is normalized, wherein preprocessing comprises denoising and missing value processing; according to the chaos theory, adopting a C-C method to solve optimal time delay Td and an optimal embedded dimension md for processed electric energy quality steady-state index time data, and carrying out phase space reconstruction to obtain one group of new multidimensional data space; on the basis of a least square support vector machine model, training the reconstructed data space to obtain an optimal prediction model; and utilizing the trained least square support vector machine model to predict the electric energy quality steady-state index to obtain prediction output. By use of the method, the problems that existing electric energy quality steady-state index prediction accuracy is low and the like are solved.

The invention provides a load forecasting method for a cloud computing center host, belongs to the cloud computing field and solves the problem that as virtual machines of different users work on a host of a cloud computing center and the host load is subject to more complicated changes, the host load has to be accurately forecasted to further dispatch the virtual machines so as to achieve the purposes of load balancing and energy consumption reduction. The core of the algorithm lies in that the phase-space reconstruction method in the chaos theory and the data grouping treatment algorithm based on genetic algorithm are combined together. Compared with the present existing method, by adopting the method provided by the invention, a smaller relative error can be obtained. Besides, under the condition that the forecasting time is prolonged, compared with the traditional method, the accumulated error of the forecasting is reduced.

The invention request to protect an information propagation prediction model based on the chaotic theory, and belongs to the information propagation analysis field; the model is formed by the flowing steps: obtaining true data sources from social networks, building a user-static multidimensional forwarding factor attribute mechanism, predicting user dynamic behavior characteristics, and building a hot topic propagation model. The following steps are listed: firstly, obtaining related data, and obtaining a data set; secondarily, extracting various behavior characteristics affecting the user from the user, information and user relation angles, and quantifying the information propagation probability; then, using the chaotic theory to predict user dynamic behaviors; finally, combining information diffusion and infectious disease propagating similar propagation mechanisms on the basis of a conventional infectious disease SIR model, fully considering the dynamic behavior characteristics, and improving so as to obtain the information propagation model based on the chaotic theory and user behaviors. The method and model can effectively represent the information propagation dynamic trends in the social networks, thus finding the important influence factors in information propagation.

The invention discloses a fuzzy entropy-based noisy signal processing method and an iterative singular spectrum (SSA) soft threshold denoising method. The method is suitable for noisy signals. Assuming that the noisy signal of length N xin = {x1, x2, ..., xN} and assuming that the additive white noise therein is uncorrelated with the signal, a d-dimensional vector is constructed and the similarityand fuzzy probability are defined by utilizing an original signal xin; a (d + 1)-dimensional vector is constructed and the corresponding similarity and fuzzy probability are defined by the same method; and the fuzzy entropy is defined in the drawing of the description. For components obtained by utilizing a known signal decomposition method, the singular spectrum distribution of all the components is defined as a fuzzy entropy spectrum. The fuzzy entropy for quantifying the complexity of the system in a chaos theory is utilized to characterize a noise plane and provide a more effective path for the processing of the noisy signal; the fuzzy entropy spectrum-based iterative singular spectrum (SSA-IST) soft threshold denoising method has the denoising performance better than that of the traditional truncated singular spectrum method, and wavelet transform and empirical mode decomposition denoising method.

The present invention provides a system for analyzing a time series signal by a method of Chaos Theory and calculating a chaos theoretical exponent value. It is a chaos theoretical exponent value calculation system comprising: a means for receiving an input of predetermined parameters, a means for reading a time series signal, a means for cutting out from the read time series signal a time series signal for each processing unit x=x(i), a means for calculating a chaos theoretical exponent value of the read time series signal, and a means for outputting a chaos theoretical exponent value of the calculated time series signal, wherein the means for calculating a chaos theoretical exponent value comprises: a means for calculating a chaos theoretical exponent value for a sampling time in a time series signal of the cut-out processing unit x=x(i), and a means for calculating, based on the chaos theoretical exponent value for the sampling time, a chaos theoretical exponent value of a time series signal for a predetermined time.

The invention discloses a chaos theory-based digital image parallel encryption method comprising the following steps: a chaotic sequence is used for subjecting a plaintext image to overall position scrambling operation, the plaintext image is divided into a number of parts, each part of the plaintext image is subjected to parallel local dispersal operation, a last pixel of each part of the plaintext image and a first pixel of a next part of the plaintext image are swapped, and the last step is repeated till overall dispersal of the plaintext image is completed. Compared with technologies of the prior art, the chaos theory-based digital image parallel encryption method is advantageous in that differential attacks and statistical characteristic analysis can be well resisted, chose-plaintext or cryptograph attacks can be also effectively resisted, large secret key space and good encryption effect can be exerted, and parallel calculating capability of a computer multinucleated cpu can be effectively used for effectively improving calculating speed and encryption performance.

The invention discloses a traffic state prediction method based on the chaos theory and a device thereof. The traffic state prediction method comprises the steps that data stream of traffic roads are acquired so that time sequences of multiple traffic parameters are obtained; multi-parameter phase space reconstruction is performed according to the time sequences of the traffic parameters and multi-parameter phase space is obtained, and optimal fusion of phase points is performed through combination of the Bayes estimation theory in the multi-parameter phase space so that fused phase space corresponding to the multiple traffic parameters is obtained; chaos analysis is performed on the time sequences in the fused phase space, and chaos prediction is performed on the traffic roads through combination of an RBF neural network when the time sequences of the fused phase space present chaos characteristics through analysis. Compared with a conventional single-parameter time sequence prediction method, a better prediction effect can be acquired by the traffic state prediction method so that predictability and precision of the traffic state prediction method are relatively high.

The invention discloses a construction method of a concrete dam deformation safety monitoring model, specifically, on the basis that by using the historical data of dam deformation observation, a stepwise regression model is established, a shuffled frog leaping algorithm (SFLA) with local optimization performance and global optimization performance is adopted to determine a weight coefficient of each sub-model, an inversion analysis method of dam prototype data is utilized to determine physical and mechanical parameters of a dam, a frog leaping hybrid model is established, and then a concretedam deformation safety monitoring model is obtained. According to the method, the residual error is analyzed and predicted by using the chaos theory, and the residual error prediction item is added tothe leapfrog prediction hybrid model, so that the problem that the fitting effect is good and the prediction result is poor due to the fact that the influence of the fitting residual error is not considered in a conventional dam displacement monitoring model is effectively solved.

The invention discloses an electric wire extrusion molding process parameter optimization method for vehicles based on a chaotic wavelet neural network, and relates to the field of production process condition optimization of electric wires for vehicles. The method comprises the following steps: firstly establishing a wavelet neural network prediction model which takes the extrusion flow rate of plastics, the main traction speed of a system and the cooling temperature of an extruding machine as input quantities, optimizing an initial parameter of the prediction model according to a chaos theory, and also introducing a chaos mechanism into a model algorithm to ensure that a learning process of network parameters has characteristics of chaotic dynamics; then learning and training the model to obtain a stable prediction model; and by taking the stable prediction model as the core, taking wire diameters and surface smoothness expected by the system as expected output values, and taking the acquisition of optimal process parameters as a target, performing optimization search by using the chaos algorithm for two times to finally find a group of optimal process parameters. By adopting the electric wire extrusion molding process parameter optimization method disclosed by the invention, wire diameter values and surface smoothness values of produced electric wires can be predicted, and key factors which influence the quality of the produced electric wires can be reasonably controlled according to a predicted value so as to ensure that the production efficiency can be effectively improved.

The invention provides a digital watermark encryption realization method. On the basis of the research of a digital watermark embedding algorithm in a wavelet domain, the chaos theory is introduced into the establishment of the digital watermark; the algorithm that a chaos sequence which is generated by Logistic mapping is embedded into a wavelet domain digital watermark is proposed and realized; and the algorithm has the characteristics of secret key uniqueness, irreversibility, invisibility and robustness according to the specific unpredictability of the chaos sequence. An experiment proves that the algorithm has a good visual effect and is a practical and feasible digital watermark encryption algorithm.

Based on chaos principle about sensitive dependence to initial value, infinite looped key stream is generated providing very high security. Length of cryptographic key is variable, could be any length theoretically (recommend 64-4096). Initial process makes subtle change of cryptographic key lead to non-correlative cryptographs. The random number agitation method in the invention adds random unpredictable information into encrypting process. Thus, even plaintext is same, but the cryptographs are different in each time. Thus, it is not possible to attack and explain the cryptograph. Moreover, the time for the method is not dependent to length of the cryptographic key. Thus, the speed is higher than other methods with same intension. The invention is suitable to various platforms. Wide application prospects are used in Internet and handphone.

The invention relates to a chaotic control method based on a feedback linearization theory. The method comprises the following steps of: (1) designing a dynamic linearization model monopolar inversion topological circuit of a dynamic system by using the feedback linearization theory; (2) performing coordinate conversion on a nonlinear system which can be subjected to input and output linearization; (3) performing feedback linearization on the external state of the system through state feedback; (4) designing a chaotic vibration controller u; and (5) selecting appropriate feedback gain and pole assignment to stably control the system. The chaotic control method has the advantages that: 1, different from the general linearization, the feedback linearization fulfills the aim of linearizationthrough strict state conversion and feedback instead of linear approximation of the dynamic characteristic; and 2, the controlled variable can be applied at any moment, and the control method is highin robustness when a model error and measurement noise are generated.

The invention discloses a synchronous frequency hopping sequence predication method based on a chaos theory. The method includes the following steps that first, a prediction model is defined; second, an error model is defined; third, the embedded dimension and delay time are determined; fourth, step hopping fragments are removed; fifth, a radial basis function prediction model is established; sixth, a formula and a result are predicted. By means of the synchronous frequency hopping sequence predication method based on the chaos theory, scarce synchronous codes are filtered out, a chaos sequence is left, then, the chaos sequence prediction theory is predicted, and the prediction performance and the overall prediction hit rate of frequency hopping information codes are improved.

The invention provides a high PSNR fragile watermarking method based on a chaos theory. The method includes the following steps of S1 embedding a water mark into an original image; and S2 extracting the water mark from the image embedded with the water mark, changing the position, and recovering the image. The high PSNR fragile watermarking method overcomes all the defects in the original scheme, and meanwhile the storage space required for the watermark does not increase, so that high PSNR can be maintained. The chaotic mapping is used for guaranteeing the safety; a block division theory is used for improving the change area and creating the storage space of recovering the data; and the recovery result of the target image is improved by the edge matching method based on VQ. An experiment shows that the method is safe and effective, and the recovery capability is satisfactory.

The invention is suitable for a field of information technology and provides a model parameter optimizing method and device. The method includes obtaining sample data and performing standardizing treatment on the sample data; obtaining the optimal penalty coefficient C and the optimal kernel scale Gamma of a kernel extreme learning machine through combining with a Moth optimization algorithm of chaos theory by adopting the sample data subjected to standardization; according to the sample data subjected to standardization, the optimal penalty coefficient C and the optimal kernel scale Gamma, constructing a target classification predication model. The invention solves a problem that the optimal penalty coefficient C and the optimal kernel scale Gamma cannot be obtained by utilizing a grid searching method in the prior art and is beneficial to improvement of effect of the constructed model in classification and predication of determined problems.