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An adaptive and aggressively bounded convergence algorithm for enhancing and measuring the performance of applications running on networks with heavy-tailed distributions

机译：自适应且积极有界的收敛算法，用于增强和衡量在具有重尾分布的网络上运行的应用程序的性能

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《》|2001年|P.1816-1823|共8页
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Wong; J.H.C.; Ip; M.T.W.; Wong; A.K.Y.;
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中图分类无线电电子学、电信技术 ;
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5. An Adaptive and Aggressively Bounded Convergence Algorithm for Enhancing and Measuring the Performance of Applications Running on Networks with Heavy-Tailed Distributions [C] . Joseph H.C. Wong, May T. W. Ip, Allan K.Y. Wong International Parallel Distributed Processing Symposium . 2001

机译：一种适应性和积极的有限收敛算法，用于增强和测量与重型分布在网络上运行的应用程序性能的影响
6. Asymmetric heavy-tailed distributions: Theory and applications to finance and risk management. [D] . Zhu, Dongming. 2007

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7. Optimal Randomness for Stochastic Configuration Network (SCN) with Heavy-Tailed Distributions [O] . Haoyu Niu, Jiamin Wei, YangQuan Chen 2021

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8. An adaptive and aggressively bounded convergence algorithm for enhancing and measuring the performance of applications running on networks with heavy-tailed distributions [O] . Wong JHC, Ip MTW, Wong AKY 2001

机译：一种自适应且有界约束的收敛算法，用于增强和衡量在具有重尾分布的网络上运行的应用程序的性能

An adaptive and aggressively bounded convergence algorithm for enhancing and measuring the performance of applications running on networks with heavy-tailed distributions

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