7 results about "Polynomial kernel" patented technology

The invention discloses a laying hen lossless data processing method based on improved XGBoost, and relates to the technical field of data processing, discrete feature numeralization is realized through adaptive tag coding, abnormal value detection and non-linear mapping correction are combined, a robustness purification feature set is constructed, and a layer lossless data processing method based on the improved XGBoost is obtained. The problems that a traditional method is sensitive to sensor noisy points and lacks an effective data cleaning mechanism are solved, and prediction precision reduction caused by extreme sample interference is avoided. And performing nonlinear expansion on the purification features by using a polynomial kernel, and accurately capturing a coupling relationship between the features. The global optimal hyper-parameter optimization of the XGBoost integrated regression model is driven by high-dimensional features, so that the parameter optimization efficiency is improved, local optimum is avoided, the method adapts to a multi-source heterogeneous and high-noise nonlinear data scene of laying hen breeding, the processing precision and generalization ability of the model are remarkably improved, reliable data support is provided for accurate management and quality monitoring of laying hen breeding, and the method is suitable for large-scale popularization and application. And efficient landing of the intelligent breeding technology is promoted.

The present application belongs to the field of building, especially a kind of rigid-flexible combination bagged pile vertical bearing capacity calculation method and system, the method specifically includes: obtaining the initial parameter set that influences pile vertical bearing capacity, including pile geometry, bag performance, soil mechanical parameters, and the one-dimensional control point vector of bottom expansion form extracted based on point cloud data reconstruction by non-uniform rational B spline, divided into side resistance parameter subset and end resistance parameter subset;Using historical sample training set, construct the hybrid kernel matrix of polynomial kernel and radial basis kernel combination for two subsets respectively, and weight according to the sensitivity difference of parameter to resistance;Mean centering and feature decomposition are carried out to the hybrid kernel matrix, and the principal component is selected according to the cumulative variance contribution rate and parameter correlation coefficient absolute value and threshold value rule, and the core factor of side resistance and end resistance is obtained by decoupling;Gaussian process regression model is established respectively with the two core factors, the side friction resistance and end resistance are predicted, and the sum is obtained to obtain the vertical bearing capacity of pile.

The invention relates to the technical field of fault detection, and discloses a transformer substation fault detection method and system, and the method comprises the steps: carrying out the windowing processing of collected time series data of a transformer substation, extracting a first feature vector, and calculating a second feature vector; constructing a mixed kernel function formed by weighted combination of a radial basis kernel function and a polynomial kernel function, and calculating weight coefficients of the radial basis kernel function and the polynomial kernel function; based on a preset model parameter and a mixed kernel function, training a single-class support vector machine by using the training sample under a normal working condition to obtain a fault decision boundary; calculating an early warning threshold, and setting an early warning boundary in the fault decision boundary; and performing feature extraction which is the same as that of the training sample on to-be-detected data, and inputting the to-be-detected data into the trained single-class support vector machine to judge the state of the to-be-detected data. According to the invention, the accuracy of fault detection is improved, the early symptom of equipment state deterioration can be identified, and early warning information is provided for operation and maintenance personnel.

The application discloses a genomic selection method for machine learning of additive and non-additive effects, and by adding non-additive effects to a GBLUP model, the prediction accuracy of the GBLUP model is greatly improved. By applying a kernel function in the GBLUP, the model can better simulate the complex genetic relationship between genes and the nonlinear combination of gene effects, thereby improving the explanation ability of genetic variation. In addition, a machine learning strategy K P RR is constructed based on a polynomial kernel function and a kernel ridge regression. By combining the advantages of the polynomial kernel function and the kernel ridge regression method, the K P RR strategy allows remote data points to contribute to the value of the model, which enables the interaction in the genomic data to be more effectively contributed, and the genomic prediction effect can be greatly improved.

This invention relates to the field of fault detection technology and discloses a substation fault detection method and system. The method includes: windowing the collected substation time-series data to extract a first feature vector and calculate a second feature vector; constructing a hybrid kernel function composed of a weighted combination of radial basis function (RBF) kernel functions and polynomial kernel functions, and calculating the weight coefficients of the RBF kernel function and polynomial kernel function; training a single-class support vector machine (SVM) using training samples under normal operating conditions based on preset model parameters and the hybrid kernel function to obtain the fault decision boundary; calculating an early warning threshold and setting an early warning boundary within the fault decision boundary; and performing the same feature extraction on the test data as on the training samples and inputting it into the trained SVM to determine the state of the test data. This invention improves the accuracy of fault detection, can identify early signs of equipment deterioration, and provides early warning information for operation and maintenance personnel.

The invention discloses a polynomial kernel-based support vector regression model reverse inversion simulation parameter optimal value method, and relates to the technical field of numerical simulation parameter calibration. According to the method, a data set is constructed by designing a simulation parameter value combination, a prediction model is constructed by adopting polynomial kernel function support vector regression (Poly-SVR), hyper-parameter optimization is realized by combining 5-fold cross validation and grid search, and finally, reverse mapping of a response quantity and a simulation parameter is completed by utilizing an L-BFGS-B algorithm. The method solves the problems that a traditional response surface method is low in calibration precision and high in overfitting risk in a small sample scene, achieves efficient and accurate inversion of simulation parameters, and is suitable for various engineering application scenes where the simulation parameters need to be reversely determined through the actual measurement response quantity.

The invention relates to a load prediction multidimensional data preprocessing method. The method comprises the following steps: firstly, acquiring a load data time sequence formed by multi-dimensional load data; then decomposing the load data time sequence to obtain a trend component which refers to a long-term trend sequence in the load data time sequence; obtaining an inflection point of the load data time sequence according to the trend component; approximation is carried out on the load data at the non-inflection point by using a linear kernel function, approximation is carried out on the load data at the inflection point by using a preset adaptive polynomial kernel function, and then a first kernel function is obtained according to the linear kernel function and the adaptive polynomial kernel function; constructing a kernel matrix according to the first kernel function; and further performing non-linear analysis on the kernel matrix by using a factor analysis method to obtain load data after dimension reduction, and finally achieving the purposes of simplifying observation data and explaining and researching complex problems by using a small number of variables.