Internet尽力而为服务模式在支持群组命令传输过程中,容易产生资源竞争问题,从而导致部分命令传输丢失造成群组命令传输失败.提出基于Internet构建有效路径统计网络(Effective Path Statistics Network,EPSN),把基于Internet网络群组命...Internet尽力而为服务模式在支持群组命令传输过程中,容易产生资源竞争问题,从而导致部分命令传输丢失造成群组命令传输失败.提出基于Internet构建有效路径统计网络(Effective Path Statistics Network,EPSN),把基于Internet网络群组命令传输问题转换成基于EPSN网络群组多约束多目标优化问题(Group Multi-Constraints Multi-Objective Optimization Problem,GM CM OOP).提出基于独占区域粒子群优化算法(Particle Sw arm Optimization based on Sole Zone,PSOSZ).该算法,根据独占搜索空间划分思想搜索GMCMOOP问题的解.实验表明,在群组命令规模分别在最大量175和最小量75下,该模型在基于Internet环境部署的EPSN网络规模不断变化下,GCT成功率相对于经典路由算法DSA和Yen有较好性能,同时误差率相对基本粒子群有较好性能.展开更多
The authors extend the notion of statistical structure from Riemannian geometry to the general framework of path spaces endowed with a nonlinear connection and a generalized metric.Two particular cases of statistical ...The authors extend the notion of statistical structure from Riemannian geometry to the general framework of path spaces endowed with a nonlinear connection and a generalized metric.Two particular cases of statistical data are defined.The existence and uniqueness of a nonlinear connection corresponding to these classes is proved.Two Koszul tensors are introduced in accordance with the Riemannian approach.As applications,the authors treat the Finslerian (α,β)-metrics and the Beil metrics used in relativity and field theories while the support Riemannian metric is the Fisher-Rao metric of a statistical model.展开更多
文摘Internet尽力而为服务模式在支持群组命令传输过程中,容易产生资源竞争问题,从而导致部分命令传输丢失造成群组命令传输失败.提出基于Internet构建有效路径统计网络(Effective Path Statistics Network,EPSN),把基于Internet网络群组命令传输问题转换成基于EPSN网络群组多约束多目标优化问题(Group Multi-Constraints Multi-Objective Optimization Problem,GM CM OOP).提出基于独占区域粒子群优化算法(Particle Sw arm Optimization based on Sole Zone,PSOSZ).该算法,根据独占搜索空间划分思想搜索GMCMOOP问题的解.实验表明,在群组命令规模分别在最大量175和最小量75下,该模型在基于Internet环境部署的EPSN网络规模不断变化下,GCT成功率相对于经典路由算法DSA和Yen有较好性能,同时误差率相对基本粒子群有较好性能.
基金Project supported by the Romanian National Authority for Scientific Research,CNCS UEFISCDI(No.PN-II-ID-PCE-2012-4-0131)
文摘The authors extend the notion of statistical structure from Riemannian geometry to the general framework of path spaces endowed with a nonlinear connection and a generalized metric.Two particular cases of statistical data are defined.The existence and uniqueness of a nonlinear connection corresponding to these classes is proved.Two Koszul tensors are introduced in accordance with the Riemannian approach.As applications,the authors treat the Finslerian (α,β)-metrics and the Beil metrics used in relativity and field theories while the support Riemannian metric is the Fisher-Rao metric of a statistical model.
