As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorit...As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.展开更多
The pruning algorithms for sparse least squares support vector regression machine are common methods, and easily com- prehensible, but the computational burden in the training phase is heavy due to the retraining in p...The pruning algorithms for sparse least squares support vector regression machine are common methods, and easily com- prehensible, but the computational burden in the training phase is heavy due to the retraining in performing the pruning process, which is not favorable for their applications. To this end, an im- proved scheme is proposed to accelerate sparse least squares support vector regression machine. A major advantage of this new scheme is based on the iterative methodology, which uses the previous training results instead of retraining, and its feasibility is strictly verified theoretically. Finally, experiments on bench- mark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms, and this speedup scheme is also extended to classification problem.展开更多
The radial deformation design of turbine disk seriously influences the control of gas turbine high pressure turbine(HPT) blade-tip radial running clearance(BTRRC). To improve the design of BTRRC under continuous opera...The radial deformation design of turbine disk seriously influences the control of gas turbine high pressure turbine(HPT) blade-tip radial running clearance(BTRRC). To improve the design of BTRRC under continuous operation, the nonlinear dynamic reliability optimization of disk radial deformation was implemented based on extremum response surface method(ERSM), including ERSM-based quadratic function(QF-ERSM) and ERSM-based support vector machine of regression(SR-ERSM). The mathematical models of the two methods were established and the framework of reliability-based dynamic design optimization was developed. The numerical experiments demonstrate that the proposed optimization methods have the promising potential in reducing additional design samples and improving computational efficiency with acceptable precision, in which the SR-ERSM emerges more obviously. Through the case study, we find that disk radial deformation is reduced by about 6.5×10–5 m; δ=1.31×10–3 m is optimal for turbine disk radial deformation design and the proposed methods are verified again. The presented efforts provide an effective optimization method for the nonlinear transient design of motion structures for further research, and enrich mechanical reliability design theory.展开更多
基金supported by the National Natural Science Foundation of China (61074127)
文摘As the solutions of the least squares support vector regression machine （LS-SVRM） are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance.
基金supported by the National Natural Science Foundation of China(50576033)
文摘The pruning algorithms for sparse least squares support vector regression machine are common methods, and easily com- prehensible, but the computational burden in the training phase is heavy due to the retraining in performing the pruning process, which is not favorable for their applications. To this end, an im- proved scheme is proposed to accelerate sparse least squares support vector regression machine. A major advantage of this new scheme is based on the iterative methodology, which uses the previous training results instead of retraining, and its feasibility is strictly verified theoretically. Finally, experiments on bench- mark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms, and this speedup scheme is also extended to classification problem.
基金Project(51275024)supported by the National Natural Science Foundations of ChinaProject(2015M580037)supported by China’s Postdoctoral Science FundingProjects(XJ2015002,G-YZ90)supported by Hong Kong Scholars Program Foundations,China
文摘The radial deformation design of turbine disk seriously influences the control of gas turbine high pressure turbine(HPT) blade-tip radial running clearance(BTRRC). To improve the design of BTRRC under continuous operation, the nonlinear dynamic reliability optimization of disk radial deformation was implemented based on extremum response surface method(ERSM), including ERSM-based quadratic function(QF-ERSM) and ERSM-based support vector machine of regression(SR-ERSM). The mathematical models of the two methods were established and the framework of reliability-based dynamic design optimization was developed. The numerical experiments demonstrate that the proposed optimization methods have the promising potential in reducing additional design samples and improving computational efficiency with acceptable precision, in which the SR-ERSM emerges more obviously. Through the case study, we find that disk radial deformation is reduced by about 6.5×10–5 m; δ=1.31×10–3 m is optimal for turbine disk radial deformation design and the proposed methods are verified again. The presented efforts provide an effective optimization method for the nonlinear transient design of motion structures for further research, and enrich mechanical reliability design theory.
