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32 results about "Vine copula" patented technology

A vine is a graphical tool for labeling constraints in high-dimensional probability distributions. A regular vine is a special case for which all constraints are two-dimensional or conditional two-dimensional. Regular vines generalize trees, and are themselves specializations of Cantor trees. Combined with bivariate copulas, regular vines have proven to be a flexible tool in high-dimensional dependence modeling. Copulas are multivariate distributions with uniform univariate margins. Representing a joint distribution as univariate margins plus copulas allows the separation of the problems of estimating univariate distributions from the problems of estimating dependence. This is handy in as much as univariate distributions in many cases can be adequately estimated from data, whereas dependence information is rough known, involving summary indicators and judgment. Although the number of parametric multivariate copula families with flexible dependence is limited, there are many parametric families of bivariate copulas. Regular vines owe their increasing popularity to the fact that they leverage from bivariate copulas and enable extensions to arbitrary dimensions. Sampling theory and estimation theory for regular vines are well developed and model inference has left the post . Regular vines have proven useful in other problems such as (constrained) sampling of correlation matrices, building non-parametric continuous Bayesian networks.

Product failure behavior coupling modeling and reliability evaluation method

InactiveCN106650204AAvoid Numerical Simulation Solving ProblemsEfficient handling of reliable life estimatesInformaticsSpecial data processing applicationsCouplingFailure mechanism
A product failure behavior coupling modeling and reliability evaluation method, namely, a product failure behavior coupling modeling and reliability evaluation method based on Vine-Copula and accelerated degradation testing, includes the following steps: 1, degradation trajectory modeling; 2, failure mechanism consistency testing; 3, distribution model making of performance parameters under normal stress with the extrapolation method; 4, failure behavior coupling modeling based on Vine-Copula; 5, reliability evaluation. Through the steps, product failure behavior coupling modeling is completed, a model with joint distribution of reliability degree is established, the problem of solving the reliability function with immediate integration is solved, the product multivariate coupling relationship is clearly described, and the method for reliability evaluation of products with multi-performance parameter correlated characteristics is provided.
Owner:BEIHANG UNIV

Product multidimensional correlation-oriented degradation failure modeling method

The present invention discloses a product multidimensional correlation-oriented degradation failure modeling method. The method specifically comprises: determining reliability characteristic quantities of a product; collecting and processing degradation data of each characteristic quantity; establishing a degradation model of each characteristic quantity; estimating a parameter in the degradation model of each characteristic quantity; establishing a multidimensional correlation degradation model by using D-Vine Copula; and determining types and parameter values of all bivariate Copula functions. According to the method provided by the present invention, a multidimensional correlation variable joint density function and an edge density function thereof are connected by a plurality of bivariate Copula functions by using D-Vine Copula, so as to effectively process a multidimensional degradation process with complex correlation, and achieve better flexibility and generalization ability.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA

Multi-modal process fault detection method and system based on vine copula correlation description

The invention discloses a multi-modal process fault detection method and system based on vine copula correlation description. The detection method comprises the following steps: obtaining a training sample set of normal data under different modals according to expert knowledge or adopting a clustering method; carrying out correlation modeling by utilizing C-vine copula and obtaining a joint probability density function of each modal; carrying out sampling on the joint probability density functions of different modals by utilizing a Markov Monte Carlo method and calculating the value of the joint probability density function of each sample; determining discretization step length l according to control limits and establishing a static density quantile table of the process by utilizing a density quantile method; estimating general local probability index PL<k> (Xt<monitor>), under the modal k, of monitoring data Xt<monitor> at the moment t by searching the table; and calculating BIP index by adopting Bayesian reasoning, and judging whether the index is beyond the limit so as to achieve real-time process monitoring.
Owner:EAST CHINA UNIV OF SCI & TECH

Monthly runoff simulation method based on D-vine copula function

ActiveCN108898250AOvercome linearityOvercoming problems such as steady stateForecastingVine copulaCorrelation test
The invention provides a monthly runoff simulation method based on a D-vine copula function. The method includes: adopting a graphical method to construct a monthly runoff density distribution function fitting model; using a mixed D-vine copula function to construct a monthly runoff dependent structure simulation model of each month; adopting the Kendall coefficient based correlation test method to determine the mixed D-vine copula dimension of each monthly runoff; using the order method based on the maximum likelihood method and the AIC method to derive D-vine copula parameters; and using a conditional function to construct a monthly runoff stimulation model based on a conditional mixed D-vine copula joint distribution function. The invention constructs the monthly runoff simulation modelbased on the mixed D-vine copula. The model constructs the relevant structure of each month according to Kendall's tau values; if the confidence value is more than 95%, correlation is determined; andthe mixed D-vine copula is used to calculate the relevant structure to overcome the problem of the linearity and a steady state of the conventional models, and the simulation result is more accurateand reliable.
Owner:HOHAI UNIV

Bearing residual life prediction method based on ternary Wiener process

The invention discloses a method for predicting the residual life of a bearing based on a ternary Wiener process. The method comprises the following steps: S1, collecting two vibration signals in mutually vertical directions and a temperature signal in a bearing degradation stage; s2, calculating the effective values of the vibration signals in the two directions and the average value of the temperature signals, and constructing three performance indexes representing the health state of the bearing; S3, checking and analyzing the three performance indexes, and judging whether the degradation process of the three performance indexes can be described by using a Wiener process or not; s4, decomposing the joint probability density function of the three performance indexes into three binary Copula functions by utilizing a Vine Copula function, and processing the three binary Copula functions, a Copula function is selected through an AIC information criterion to describe related characteristics among all performance indexes, a joint probability density function of the residual life of the bearing is obtained, model parameters are updated on line through a step-by-step maximum likelihoodestimation method, and the residual life of the bearing is predicted. The method is high in prediction precision and requires less training data.
Owner:宁海县浙工大科学技术研究院

Mechanism time-variation reliability analysis method

The invention discloses a mechanism time-variation reliability analysis method, which is applied to the field of mechanism time-variation reliability. For the problem that actual engineering reliability cannot be described completely and accurately in the prior art, effective diffusion points on upper and lower boundaries are solved through an envelope equation; uncertainty random information of non-normal distribution is adopted as a random variable input; kinematic error sample points corresponding to the diffusion points under the uncertainty random variable input are obtained; correlationof the kinematic error sample points corresponding to the diffusion points is described through a copula function; and multidimensional joint probability density functions corresponding to the diffusion points are built through a characteristic of multidimensional distribution function decomposition of a vine-copula function, so that a definite integral is solved for obtaining the time-variation reliability. Therefore, a conventional normal distribution hypothesis is popularized as the non-normal distribution; and the method has relatively high practical values and relatively strong engineering significance.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA

Multi-sensor Vine Copula heterogeneous information decision fusion method

The invention discloses a multi-sensor Vine Copula heterogeneous information decision fusion method, and belongs to the field of information fusion. According to the method, a heterogeneous sensor Vine copula decision fusion algorithm is provided, different types of sensor data are unified into the same event space through feature extraction and event driving, and an event set corresponding to a target is established based on data features and prior information of the target; and modeling is carried out on the correlation between sensor data feature events through Vine copula. According to themethod, the correlation between the heterogeneous information fusion feature events can be flexibly constructed, more accurate event joint probability distribution is generated, and the decision fusion performance of heterogeneous sensors is improved; numerical analysis shows that the new method provided by the invention has a better decision-making effect on a target classification and identification problem, and can fuse heterogeneous sensor information more scientifically and reasonably.
Owner:SICHUAN UNIV

Industrial production simulation scene generator and scene generation method based on vine-copulas

ActiveCN110471279AReliable raw dataSupport scene simulation functionAdaptive controlFeature DimensionDimensionality reduction
The invention provides an industrial production simulation scene generator and a scene generation method based on vine-copulas. The industrial production simulation scene generator comprises a scene data acquisition module, a scene preprocessing module, a Vine-copulas creation module and a scene generation module. In the scene generation method, the existing scene data is acquired from an initialscene database module, by preprocessing of a scene clustering module, a feature standardization module and a feature dimension reduction module, and so on, a scene generation model is created througha vine copula method through a model fitting module, a goodness detection module and a binary copula model library module, and the scene needed is generated through a scene sampling module and an inverse mapping module. The scene generator provided by the invention can be embedded into an emulation system to be used as an organization module of a production simulation system and used for configuration operations of the production simulation system. The scene generated by the method provided by the invention can be used for scene emulation, and also can provide reliable original data for otherexperiments and researches to perform targeted study.
Owner:ZHEJIANG UNIV +1

Method for determining probability optimal power flow based on vine copula function

The invention discloses a method for determining a probability optimal power flow based on a vine copula function. The method comprises the following steps that the minimum power generation cost serves as a target function, and a probability optimal power flow model containing the multi-wind electric field is built; a single wind power plant wind speed distribution function is established according to the non-parameter nuclear density fitting wind speed; a single wind power plant wind speed distribution function and a vine copula function are utilized, and a multi-wind power plant wind speed combined distribution model is built; M scenarios of loads and wind power and the probability of occurrence of each situation can be determined based on a multi-wind power plant wind speed combined distribution model and by means of a Rosenblat transformation and a three-point estimation method; according to the probability optimal power flow model of the multi-wind power plant, the optimal power flow under the M scenes can be calculated by utilizing an internal point method; the probability optimal power flow is obtained according to the optimal power flow under the m scenarios and the probability of each situation. According to the invention, the diversified correlation structure of the wind speed is effectively described, and the calculation efficiency is high, so that effective information can be provided for the operation of the electric power system in time.
Owner:HUAZHONG UNIV OF SCI & TECH

Hydrological dependent structure modeling method based on mutual information and vine copula

The invention discloses a hydrological dependent structure modeling method based on mutual information and vine copula. Firstly, mutual information and conditional mutual information are used to measure the correlation and uncertainty of hydrological variables, in combination with the principle of the strongest correlation and the least uncertainty, the structure of vine copula is selected, starting from the first tree, the mutual information of pairs of paired variables is calculated, the pairing mode that maximizes the sum of mutual information is selected as the edge of the tree, the conditional mutual information of possible pairing variables is calculated, and the pairing mode that maximizes the sum of conditional mutual information is selected as tree 2, and the pairing mode is repeated until the structure of the whole tree is determined. Secondly, according to the tree structure, fitting of edge distribution is carried out, a goodness-of-fit test is carried out, starting from tree 1, the AIC criterion is utilized to determine the copula type of the edge, parameters are estimated, a goodness-of-fit test is performed, then the conditional edge distribution of variables is calculated, and the determination of copula type, estimating parameters and testing steps are repeated until all the trees are determined. All trees and edges are connected to complete the modeling of hydrological dependent structures.
Owner:NANJING UNIV

Soft measurement method and system for vine copula correlation description based on Hamiltonian Monte Carlo sampling

The invention provides a soft measurement method and a system for vine copula correlation description based on Hamiltonian Monte Carlo sampling. The method comprises the following steps: selecting anappropriate auxiliary variable for a soft measurement model; performing standardization and monotonous transformation on the training data, and calculating an average variance of target variables of the training data; c-vine copula is used for carrying out correlation modeling; carrying out online collection, standardization processing and monotonic transformation calculation on auxiliary variables of a to-be-predicted sample; performing Hamiltonian Monte Carlo sampling according to the distribution of the target variables of the training sample; calculating a copula function value of the processed auxiliary variable of the to-be-predicted sample and the sampling sample, and further calculating conditional probabilities of all possible results of the target variable; obtaining a mathematical expectation of a final prediction value; determining a confidence interval of the predicted value according to the conditional probability, and calculating a variance; and comparing whether the mathematical expectation variance of the prediction value exceeds the average variance of the target variable of the training sample.
Owner:EAST CHINA UNIV OF SCI & TECH

Method and system for soft measurement based on vine copula

The invention provides a method and system for soft measurement based on vine copula, and the method comprises the following steps: selecting a proper auxiliary variable for a soft measurement model according to the actual industrial production condition and expert knowledge; performing standardization and monotonous transformation on the training data to obtain transformed data conforming to copula modeling; performing correlation modeling by utilizing D-vine copula to obtain a joint probability density function of the training sample auxiliary variable and the target variable; carrying out online collection, standardization processing and monotonic transformation calculation on auxiliary variables of a to-be-predicted sample; calculating a copula function value of the processed auxiliaryvariable of the sample to be predicted and the target variables of all the training samples, and further calculating the weight of each training sample; and according to the calculated training sample weight, carrying out linear weighting on the target variable of the training sample to obtain a standardized prediction value of the target variable of the to-be-predicted sample, and then carryingout inverse transformation to obtain a final prediction value.
Owner:EAST CHINA UNIV OF SCI & TECH

Intermuscular coupling analysis method for R vine Copula mutual information

The invention discloses an intermuscular coupling analysis method for R vine Copula mutual information. The method comprises the following steps of: synchronously acquiring and preprocessing multichannel surface electromyogram signals; estimating a marginal distribution function by using non-parametric kernel density; performing simple matrix representation and parameter estimation of the R vine Copula, and estimating R vine Copula mutual information and R vine Copula conditional mutual information at the same time; and finally, performing intermuscular coupling analysis. The RVCMI and the RVCCM I provided by the invention provide a new research method and a scientific theoretical basis for intermuscular coupling analysis, and have a good application prospect.
Owner:HANGZHOU DIANZI UNIV

Multivariable causal relationship method based on R-vine Copula transfer entropy

According to the method, on the basis of the transfer entropy of a classical causal analysis method, a Copula function in statistics is combined and popularized to the transfer entropy in a high-dimensional form, and a regular vine Copula function is used for estimation, so that an effective new R-Vine Copula multivariable transfer entropy method is provided. Firstly, compared with an existing algorithm on a series of simulation data, the multivariable causal analysis performance of the method is verified. Secondly, the method is applied to multi-channel electromyographic signal analysis when different wrist movement tasks are executed. According to the application of the coupling relation between muscles, when the neuromuscular system executes different wrist movement tasks such as wrist extension and wrist radial deviation, two community structures which are related to a joint area and have a stable causal coupling relation are formed. The R-Vine Copula multivariable transfer entropy accurately deduces the complex causal coupling relationship, and has a good application value.
Owner:HANGZHOU DIANZI UNIV

A method for determining probabilistic optimal power flow based on vine copula function

The invention discloses a method for determining a probability optimal power flow based on a vine copula function. The method comprises the following steps that the minimum power generation cost serves as a target function, and a probability optimal power flow model containing the multi-wind electric field is built; a single wind power plant wind speed distribution function is established according to the non-parameter nuclear density fitting wind speed; a single wind power plant wind speed distribution function and a vine copula function are utilized, and a multi-wind power plant wind speed combined distribution model is built; M scenarios of loads and wind power and the probability of occurrence of each situation can be determined based on a multi-wind power plant wind speed combined distribution model and by means of a Rosenblat transformation and a three-point estimation method; according to the probability optimal power flow model of the multi-wind power plant, the optimal power flow under the M scenes can be calculated by utilizing an internal point method; the probability optimal power flow is obtained according to the optimal power flow under the m scenarios and the probability of each situation. According to the invention, the diversified correlation structure of the wind speed is effectively described, and the calculation efficiency is high, so that effective information can be provided for the operation of the electric power system in time.
Owner:HUAZHONG UNIV OF SCI & TECH

Station network optimization method based on high-dimensional Copula entropy and Kriging

PendingCN114595556ABest estimate errorExcellent Rainfall InformationData processing applicationsDesign optimisation/simulationAtmospheric sciencesTotal correlation
The invention discloses a station network optimization method based on high-dimensional Copula entropy and Kriging. The method comprises the following steps: (1) constructing a hydrological C-Vine Copula tree structure; (2) estimating a C-Vine Copula parameter by adopting a maximum likelihood estimation method; (3) obtaining high-dimensional mutual information through a function relationship between the multivariable mutual information and the C-Vine Copula density; and (4) optimizing the dynamic rainfall station network through a standardized MiK-MiT-MaJ index and a sliding window method. According to the method, a high-dimensional dependency structure among multiple stations is obtained by adopting C-Vine Copula, and the total amount and the total correlation amount of station network objective function information are optimized; the optimal rainfall station network estimation error and the optimal rainfall information are realized by using the Kriging standard error value; multi-objective optimization is simplified into single-objective optimization, optimization efficiency is improved, and rainfall sequence time-varying characteristics are considered to cause dynamic characteristics of a station network optimization result.
Owner:YANGZHOU UNIV

Method and device for forecasting combined output of multiple wind farms

ActiveCN112653199BImprove accuracyReflect the characteristics of differentiated associationsSingle network parallel feeding arrangementsWind energy generationAlgorithmDynamic models
The invention discloses a method and device for predicting the combined output of multiple wind farms based on a dynamic R-Vine Copula model, belonging to the field of wind power interval prediction in a power system. The method includes: S1: combining the predicted output data of multiple wind farms with The prediction error corresponding to the joint output is used as the first input data; S2: Input the first input data into the dynamic marginal distribution function model established based on the ARIMA-GARCH model, so that the dynamic marginal distribution function model converts the first input data into a cumulative probability sequence ; S3: Input the cumulative probability sequence into the pre-established dynamic R‑Vine Copula model, so that the dynamic R‑Vine Copula model outputs the joint output prediction results corresponding to multiple wind farms under different confidence levels; wherein, the model of the dynamic marginal distribution function model The parameters and model parameters of the dynamic R‑Vine Copula model are calculated and updated on a rolling basis based on the phase space reconstruction method. The present application can improve the ultra-short-term interval prediction accuracy of the combined output of multiple wind farms.
Owner:HUAZHONG UNIV OF SCI & TECH

Confidence capacity evaluation method and device based on vine copula and mixed offset normal distribution

The invention discloses a confidence capacity evaluation method and device based on vine copula and mixed offset normal distribution. The method comprises the following steps: S1, inputting all new energy power generation output historical data in a system; s2, establishing a marginal distribution model of all new energy outputs by utilizing the offset normal distribution hybrid model; s3, establishing a correlation structure model among all new energy outputs by utilizing a vine copula method; s4, generating a plurality of new energy output scenes with correlation by utilizing the marginal distribution model and the correlation structure model of the new energy output; and S5, according to the generated new energy output scene, calculating the confidence capacity of the to-be-evaluated new energy power generation resource by utilizing a secant method. According to the method, the new energy power generation output historical data can be used to establish an accurate new energy outputmodel, a new energy output scene fitting the reality is generated, and the confidence capacity of the new energy power generation resources can be accurately evaluated.
Owner:JIANGSU ELECTRIC POWER CO

Power transmission system planning method and device considering wind and light correlation, and storage medium

The invention discloses a power transmission system planning method and device considering wind and light correlation and a storage medium. The method comprises the steps of obtaining a historical data set of power system parameters and random variables; performing scene division on the historical data set according to a Mini Batch K-Means clustering method; respectively determining parameters and types of Copula functions according to the scene data, and judging optimal rattan structures in the scenes by adopting AD distance; establishing a mixed vine Copula model according to the optimal vine structure in each scene, and generating a wind-light load sample set; and inputting the wind-light load sample set and the power system parameters into the power transmission system planning model, and outputting a power grid planning result. According to the method, the Mini Batch K-Means algorithm is used for scene division, so that the operation time is shortened; the AD distance is used as a rattan structure judgment criterion, the modeling precision of the mixed rattan Copula is improved, and the credibility of the power transmission system extension planning method is further improved.
Owner:GUANGDONG POWER GRID CO LTD

Multi-wind-power-plant power modeling method and system, PDF construction method and system, and prediction scene generation method and system

ActiveCN113094891AOvercome upper tailOvercoming lower tail symmetryDesign optimisation/simulationProbabilistic CADAlgorithmEngineering
The invention discloses a multi-wind-power-plant power modeling method and system, a PDF construction method and system and a prediction scene generation method and system, and belongs to the field of scene prediction of wind power. According to the method, a probability distribution model of prediction errors is established, and the sum of the probability distribution of the prediction errors and the point prediction power of the power of each wind power plant is used as an edge distribution model of the power of each wind power plant. The cumulative probability of the power data of each wind power plant is calculated, and the cumulative probability is taken as the input data of the time-varying R-vine Copula model. The joint probability distribution model of the high-dimensional wind power data is established by combining the ARIMA-GARCH-t model and the time-varying R-vine Copula model. Model parameters are fit based on historical power data of each wind power plant, and a multi-wind power plant power day-ahead prediction scene generation method is provided by combining point prediction power data of each wind power plant in the next day on this basis. The day-ahead prediction scene generation model established by the invention can better fit the time-space correlation characteristics of the power of the multiple wind power plants, and the accuracy and effectiveness of the day-ahead prediction scene of the power of the multiple wind power plants are improved.
Owner:HUAZHONG UNIV OF SCI & TECH

Multi-wind-power-plant combined output prediction method and device based on dynamic R-Vine Copula model

ActiveCN112653199AImprove accuracyReflect the characteristics of differentiated associationsSingle network parallel feeding arrangementsWind energy generationAlgorithmElectric power system
The invention discloses a multi-wind-power-plant combined output prediction method and device based on a dynamic R-Vine Copula model, which belong to the field of wind power interval prediction in an electric power system. The method comprises the steps of S1, taking prediction output data of multiple wind power plants and prediction errors corresponding to combined output as first input data, S2, inputting the first input data into a dynamic edge distribution function model established based on an ARIMA-GARCH model, so that the dynamic edge distribution function model converts the first input data into a cumulative probability sequence, S3, inputting the cumulative probability sequence into a pre-established dynamic R-Vine Copula model so as to enable the dynamic R-Vine Copula model to output joint output prediction results corresponding to multiple wind power plants under different confidence coefficients, wherein the model parameters of the dynamic edge distribution function model and the model parameters of the dynamic R-Vine Copula model are subjected to rolling calculation and updating based on a phase space reconstruction method. Thus, the ultra-short-term interval prediction accuracy of multi-wind-power-plant combined output can be improved.
Owner:HUAZHONG UNIV OF SCI & TECH

Method and system for multi-wind farm power modeling, pdf construction, and forecast scenario generation

The invention discloses a multi-wind farm power modeling, PDF construction, and prediction scene generation method and system, belonging to the field of wind power scene prediction. The invention establishes the probability distribution model of the prediction error, and adds the probability distribution of the prediction error and the point prediction power of each wind farm power as the edge distribution model of each wind farm power. Calculate the cumulative probability of the power data of each wind farm as the input data of the time-varying R-Vine Copula model. By combining the ARIMA-GARCH-t model and the time-varying R-vine Copula model, a joint probability distribution model of high-dimensional wind power data is established. Based on the fitting model parameters of the historical power data of each wind farm, combined with the point forecast power data of each wind farm in the next day, a method for generating multi-wind farm power day-ahead forecast scenarios is proposed. The day-ahead prediction scenario generation model established by the present invention can better fit the time-space correlation characteristics of multi-wind farm power, and improve the accuracy and effectiveness of multi-wind farm power day-ahead prediction scenarios.
Owner:HUAZHONG UNIV OF SCI & TECH

Intelligent evaluation method and system for high-temperature capacity reduction state of wind turbine generator

The invention discloses an intelligent evaluation method for a high-temperature capacity reduction state of a wind turbine generator, and the method comprises the following steps: collecting multi-dimensional feature variables of the wind turbine generator, and forming a training data set of a vin-Copula Bayesian network model based on the multi-dimensional feature variables; establishing a pair-Copula function according to the training data set so as to determine an optimal Copula function between every two of the multi-dimensional characteristic variables; according to the optimal Copula function, obtaining a correlation coefficient of each group of pair-Copula functions in the optimal Copula function, constructing a relationship tree based on the correlation coefficient, and generatinga vine-Copula Bayesian network model by utilizing the relationship tree; and evaluating the test samples in the state point test sample set by using the vine-Copula Bayesian network model. The invention further discloses an intelligent evaluation system for the high-temperature capacity reduction state of the wind turbine generator. According to the method, the vine-Copula Bayesian network model is utilized to comprehensively consider the state parameters of each dimension of the characteristic variables of the wind turbine generator and the correlation of the state parameters, and the occurrence probability of the high-temperature capacity reduction state of the wind turbine generator can be accurately evaluated.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING) +1

Modeling Method of Hydrological Interdependence Structure Based on Mutual Information and Vine Copula

The invention discloses a hydrological dependent structure modeling method based on mutual information and vine copula. Firstly, mutual information and conditional mutual information are used to measure the correlation and uncertainty of hydrological variables, in combination with the principle of the strongest correlation and the least uncertainty, the structure of vine copula is selected, starting from the first tree, the mutual information of pairs of paired variables is calculated, the pairing mode that maximizes the sum of mutual information is selected as the edge of the tree, the conditional mutual information of possible pairing variables is calculated, and the pairing mode that maximizes the sum of conditional mutual information is selected as tree 2, and the pairing mode is repeated until the structure of the whole tree is determined. Secondly, according to the tree structure, fitting of edge distribution is carried out, a goodness-of-fit test is carried out, starting from tree 1, the AIC criterion is utilized to determine the copula type of the edge, parameters are estimated, a goodness-of-fit test is performed, then the conditional edge distribution of variables is calculated, and the determination of copula type, estimating parameters and testing steps are repeated until all the trees are determined. All trees and edges are connected to complete the modeling of hydrological dependent structures.
Owner:NANJING UNIV

Cortical muscle function network construction method based on R-vine Copula

The invention discloses a cortical muscle function network construction method based on R-vine Copula. According to the method, firstly, electroencephalogram signals and surface electromyogram signals from multiple subjects are synchronously recorded, and specific tasks are required to be executed according to metronome instructions; and other artifacts are separated from the collected electroencephalogram and electromyogram data by adopting independent component analysis, and de-noising is conducted by adopting a wavelet threshold. and secondly, a new cortical muscle function method for quantitatively analyzing the complex causal relationship between cortical muscle signals is constructed. According to the method, R-vine Copula is used for carrying out electroencephalogram and electromyogram nonlinear coupling analysis and brain muscle function network modeling, and a large number of experiments prove that the method can effectively describe cortical muscle connectivity in a specific walking state, and the constructed cortical muscle function network is meaningful.
Owner:睿旭康苏州智能技术有限责任公司
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