4 results about "Selection operator" patented technology

The present disclosure provides a method, an electronic device, a storage medium and a program product for establishing an OPC model. The method comprises: iteratively determining, based on a least absolute shrinkage and selection operator (LASSO) linear regression algorithm, an intermediate model threshold value and a corresponding physical effect coefficient value of the OPC model in each iteration; in response to the iteration satisfying a predetermined condition, determining a final model threshold value based on at least a comparison of a last model threshold value corresponding to a last iteration among the determined intermediate model threshold values and a predetermined threshold value; and establishing the OPC model based on the final model threshold value and the physical effect coefficient value corresponding to the final model threshold value.

The application discloses a drilling accident prediction method and device based on network optimization, equipment and medium, through multiple populations, each population evolves along different directions, the evolution in the population and the existing work remain consistent, the exchange between the populations is introduced, and the solving efficiency is improved. The application proposes a drilling accident prediction method based on BP neural network, relying on the BP neural network, the intelligent level of accident early warning is significantly enhanced; a multi-population based method is proposed to search for the optimal parameters of the BP neural network, the global search ability is enhanced, and the accuracy of the early warning is improved; the selection operator, the exchange operator and the crossover operator of the multi-population are proposed, the shortcomings of the existing genetic algorithm are improved, and the method can be widely applied to the technical field of data processing.

Disclosed is an estimation method for joint causal effects of multiple exposures based on high-dimensional independent variables, including the following steps: reducing a dimension by using a modified adaptive least absolute shrinkage and selection operator (LASSO); calculating balance weights by using a nonparametric multiple treatments covariate balancing generalized propensity score (npmtCBGPS) method, and determining an optimal value of a tuning parameter by taking a minimum multiple treatment dual-weighted coefficient (mtDWC) as a criterion; and estimating joint causal effects of multiple continuous exposure factors on an outcome variable by using an inverse probability weighting (IPW) method. According to the present invention, in a framework of a GOAL method, a multiple treatments GOAL (mtGOAL) method by combining the npmtCBGPS method with the adaptive LASSO, and a method capable of estimating joint causal effects of multiple continuous exposure factors on an outcome variable in the presence of high-dimensional covariates are proposed.

The invention provides a fireman physical fatigue identification method based on LASSO multi-feature weighting and a long and short term memory model, and belongs to the technical field of physiological signal processing and fatigue detection. The method comprises the steps that electrocardiosignals of firemen are collected for time domain analysis, frequency domain analysis and nonlinear analysis, and physical fatigue influence variables of the firemen are obtained; performing Wilcoxon symbol rank test and pairing t test on the physical fatigue influence variables of the firefighter to screen out preliminary input features; selecting key features based on the preliminary input features through a minimum absolute shrinkage and selection operator; and inputting the key features into a long-short-term memory model to output a fireman physical fatigue identification result. Sensitive HRV indexes are screened out by using a statistical test method, redundant and irrelevant features are effectively removed by combining an LASSO feature selection method, and a most representative key feature set is reserved. The data dimension is reduced, the calculation efficiency of the model is improved, and the over-fitting risk caused by feature redundancy is also avoided.