To improve the practicability, suitability and accuracy of the trade-off among time, cost and quality of a process, a method based on resource capability is introduced. Through analyzing the relationship between an ac...To improve the practicability, suitability and accuracy of the trade-off among time, cost and quality of a process, a method based on resource capability is introduced. Through analyzing the relationship between an activity and its’ supporting resource, the model trades off the time, cost and quality by changing intensity of labor or changing the types of supporting resource or units of labor of resource in a certain time respectively according to the different types of its’ supporting resources. Through contrasting this method with the model of unit time cost corresponding to different quality levels and inter-related linear programming model of time, cost and quality for process optimizing, it is shown that this model does not only cover the above two models but also can describe some conditions the above two models can not express. The method supports to select different function to optimize a process according to different types of its supporting resource.展开更多
ANN （artificial neural network） is a technique successfully employed in many applications on several research fields. An appropriate configuration for neural networks is a tedious task, and it often requires the kno...ANN （artificial neural network） is a technique successfully employed in many applications on several research fields. An appropriate configuration for neural networks is a tedious task, and it often requires the knowledge of an expert on the application. In this paper, a technique for automatic configuration for two types of neural networks is presented. The multilayer perceptron and recurrent Elman are the neural networks used here. The determination of optimal parameters for the neural network is formulated as an optimization problem, solved with the use of meta-heuristic MPCA （multiple particle collision algorithm）. The self-configuring networks are applied to perform data assimilation.展开更多
Improving numerical forecasting skill in the atmospheric and oceanic sciences by solving optimization problems is an important issue. One such method is to compute the conditional nonlinear optimal perturbation(CNOP),...Improving numerical forecasting skill in the atmospheric and oceanic sciences by solving optimization problems is an important issue. One such method is to compute the conditional nonlinear optimal perturbation(CNOP), which has been applied widely in predictability studies. In this study, the Differential Evolution(DE) algorithm, which is a derivative-free algorithm and has been applied to obtain CNOPs for exploring the uncertainty of terrestrial ecosystem processes, was employed to obtain the CNOPs for finite-dimensional optimization problems with ball constraint conditions using Burgers' equation. The aim was first to test if the CNOP calculated by the DE algorithm is similar to that computed by traditional optimization algorithms, such as the Spectral Projected Gradient(SPG2) algorithm. The second motive was to supply a possible route through which the CNOP approach can be applied in predictability studies in the atmospheric and oceanic sciences without obtaining a model adjoint system, or for optimization problems with non-differentiable cost functions. A projection skill was first explanted to the DE algorithm to calculate the CNOPs. To validate the algorithm, the SPG2 algorithm was also applied to obtain the CNOPs for the same optimization problems. The results showed that the CNOPs obtained by the DE algorithm were nearly the same as those obtained by the SPG2 algorithm in terms of their spatial distributions and nonlinear evolutions. The implication is that the DE algorithm could be employed to calculate the optimal values of optimization problems, especially for non-differentiable and nonlinear optimization problems associated with the atmospheric and oceanic sciences.展开更多
Minimax programming problems involving generalized （p, r）-invex functions are consid- ered. Parametric sufficient optimality conditions and duality results are established under the aforesaid assumptions on the obje...Minimax programming problems involving generalized （p, r）-invex functions are consid- ered. Parametric sufficient optimality conditions and duality results are established under the aforesaid assumptions on the objective and constraint functions.展开更多
The mean shift registration(MSR) algorithm is proposed to accurately estimate the biases for multiple dissimilar sensors.The new algorithm is a batch optimization procedure.The maximum likelihood estimator is used to ...The mean shift registration(MSR) algorithm is proposed to accurately estimate the biases for multiple dissimilar sensors.The new algorithm is a batch optimization procedure.The maximum likelihood estimator is used to estimate the target states,and then the mean shift algorithm is implemented to estimate the sensor biases.Monte Carlo simulations show that the MSR algorithm has significant improvement in performance with reducing the standard deviation and mean of sensor biased estimation error compared with the maximum likelihood registration algorithm.The quantitative analysis and the qualitative analysis show that the MSR algorithm has less computation than the maximum likelihood registration method.展开更多
基金Sponsored by the Natural High-Technology Development Program for CIMS, China(Grant No2001AA15010)
文摘To improve the practicability, suitability and accuracy of the trade-off among time, cost and quality of a process, a method based on resource capability is introduced. Through analyzing the relationship between an activity and its’ supporting resource, the model trades off the time, cost and quality by changing intensity of labor or changing the types of supporting resource or units of labor of resource in a certain time respectively according to the different types of its’ supporting resources. Through contrasting this method with the model of unit time cost corresponding to different quality levels and inter-related linear programming model of time, cost and quality for process optimizing, it is shown that this model does not only cover the above two models but also can describe some conditions the above two models can not express. The method supports to select different function to optimize a process according to different types of its supporting resource.
文摘ANN （artificial neural network） is a technique successfully employed in many applications on several research fields. An appropriate configuration for neural networks is a tedious task, and it often requires the knowledge of an expert on the application. In this paper, a technique for automatic configuration for two types of neural networks is presented. The multilayer perceptron and recurrent Elman are the neural networks used here. The determination of optimal parameters for the neural network is formulated as an optimization problem, solved with the use of meta-heuristic MPCA （multiple particle collision algorithm）. The self-configuring networks are applied to perform data assimilation.
基金provided by grants from the LASG State Key Laboratory Special Fundthe National Natural Science Foundation of China (Grant Nos. 40905050, 40830955, and 41375111)
文摘Improving numerical forecasting skill in the atmospheric and oceanic sciences by solving optimization problems is an important issue. One such method is to compute the conditional nonlinear optimal perturbation(CNOP), which has been applied widely in predictability studies. In this study, the Differential Evolution(DE) algorithm, which is a derivative-free algorithm and has been applied to obtain CNOPs for exploring the uncertainty of terrestrial ecosystem processes, was employed to obtain the CNOPs for finite-dimensional optimization problems with ball constraint conditions using Burgers' equation. The aim was first to test if the CNOP calculated by the DE algorithm is similar to that computed by traditional optimization algorithms, such as the Spectral Projected Gradient(SPG2) algorithm. The second motive was to supply a possible route through which the CNOP approach can be applied in predictability studies in the atmospheric and oceanic sciences without obtaining a model adjoint system, or for optimization problems with non-differentiable cost functions. A projection skill was first explanted to the DE algorithm to calculate the CNOPs. To validate the algorithm, the SPG2 algorithm was also applied to obtain the CNOPs for the same optimization problems. The results showed that the CNOPs obtained by the DE algorithm were nearly the same as those obtained by the SPG2 algorithm in terms of their spatial distributions and nonlinear evolutions. The implication is that the DE algorithm could be employed to calculate the optimal values of optimization problems, especially for non-differentiable and nonlinear optimization problems associated with the atmospheric and oceanic sciences.
文摘Minimax programming problems involving generalized （p, r）-invex functions are consid- ered. Parametric sufficient optimality conditions and duality results are established under the aforesaid assumptions on the objective and constraint functions.
基金the National Basic Research Program ofChina(No.A1420060161)the National Natural ScienceFoundation of China(No.60674107)+1 种基金the Natural ScienceFoundation of Hebei Province(No.F2006000343)the National Aviation Cooperation Research Foundationof China(No.10577012)
文摘The mean shift registration(MSR) algorithm is proposed to accurately estimate the biases for multiple dissimilar sensors.The new algorithm is a batch optimization procedure.The maximum likelihood estimator is used to estimate the target states,and then the mean shift algorithm is implemented to estimate the sensor biases.Monte Carlo simulations show that the MSR algorithm has significant improvement in performance with reducing the standard deviation and mean of sensor biased estimation error compared with the maximum likelihood registration algorithm.The quantitative analysis and the qualitative analysis show that the MSR algorithm has less computation than the maximum likelihood registration method.
