许多系统把数据访问请求当作是独立的事件。实际上,数据请求并非完全随机,而是由用户或程序的行为驱动的,不同的用户或程序存在不同的访问模式。LS(Last Successor)模型简单,但非常有效,然而它的预测结果严重依赖于用户或程序的访问顺...许多系统把数据访问请求当作是独立的事件。实际上,数据请求并非完全随机,而是由用户或程序的行为驱动的,不同的用户或程序存在不同的访问模式。LS(Last Successor)模型简单,但非常有效,然而它的预测结果严重依赖于用户或程序的访问顺序。提出了ULNS(User-based Last N Successors)文件预测模型,利用用户信息来提高预测精确度,并综合LS模型来改进算法的可适用度。实验结果表明,该预测模型具有较好的整体性能。展开更多
Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance o...Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.展开更多
基金国家自然科学基金( the National Natural Science Foundation of China under Grant No.90412017)
文摘许多系统把数据访问请求当作是独立的事件。实际上,数据请求并非完全随机,而是由用户或程序的行为驱动的,不同的用户或程序存在不同的访问模式。LS(Last Successor)模型简单,但非常有效,然而它的预测结果严重依赖于用户或程序的访问顺序。提出了ULNS(User-based Last N Successors)文件预测模型,利用用户信息来提高预测精确度,并综合LS模型来改进算法的可适用度。实验结果表明,该预测模型具有较好的整体性能。
基金Project(513300303)supported by the General Armament Department,China
文摘Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.
