A method for fast 1-fold cross validation is proposed for the regularized extreme learning machine （RELM）. The computational time of fast l-fold cross validation increases as the fold number decreases, which is oppo...A method for fast 1-fold cross validation is proposed for the regularized extreme learning machine （RELM）. The computational time of fast l-fold cross validation increases as the fold number decreases, which is opposite to that of naive 1-fold cross validation. As opposed to naive l-fold cross validation, fast l-fold cross validation takes the advantage in terms of computational time, especially for the large fold number such as l 〉 20. To corroborate the efficacy and feasibility of fast l-fold cross validation, experiments on five benchmark regression data sets are evaluated.展开更多
Cross iteration often exists in the computational process of the simulation models, especially for control models. There is a credibility defect tracing problem in the validation of models with cross iteration. In ord...Cross iteration often exists in the computational process of the simulation models, especially for control models. There is a credibility defect tracing problem in the validation of models with cross iteration. In order to resolve this problem, after the problem formulation, a validation theorem on the cross iteration is proposed, and the proof of the theorem is given under the cross iteration circumstance. Meanwhile, applying the proposed theorem, the credibility calculation algorithm is provided, and the solvent of the defect tracing is explained. Further, based on the validation theorem on the cross iteration, a validation method for simulation models with the cross iteration is proposed, which is illustrated by a flowchart step by step. Finally, a validation example of a sixdegree of freedom (DOF) flight vehicle model is provided, and the validation process is performed by using the validation method. The result analysis shows that the method is effective to obtain the credibility of the model and accomplish the defect tracing of the validation.展开更多
基金supported by the National Natural Science Foundation of China(51006052)the NUST Outstanding Scholar Supporting Program
文摘A method for fast 1-fold cross validation is proposed for the regularized extreme learning machine （RELM）. The computational time of fast l-fold cross validation increases as the fold number decreases, which is opposite to that of naive 1-fold cross validation. As opposed to naive l-fold cross validation, fast l-fold cross validation takes the advantage in terms of computational time, especially for the large fold number such as l 〉 20. To corroborate the efficacy and feasibility of fast l-fold cross validation, experiments on five benchmark regression data sets are evaluated.
基金supported by the National Natural Science Foundation of China(61374164)
文摘Cross iteration often exists in the computational process of the simulation models, especially for control models. There is a credibility defect tracing problem in the validation of models with cross iteration. In order to resolve this problem, after the problem formulation, a validation theorem on the cross iteration is proposed, and the proof of the theorem is given under the cross iteration circumstance. Meanwhile, applying the proposed theorem, the credibility calculation algorithm is provided, and the solvent of the defect tracing is explained. Further, based on the validation theorem on the cross iteration, a validation method for simulation models with the cross iteration is proposed, which is illustrated by a flowchart step by step. Finally, a validation example of a sixdegree of freedom (DOF) flight vehicle model is provided, and the validation process is performed by using the validation method. The result analysis shows that the method is effective to obtain the credibility of the model and accomplish the defect tracing of the validation.
