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101.
Segment-based adaptive hyper-Erlang model for long-tailed network traffic approximation 总被引:1,自引:1,他引:0
Modeling the long-tailedness property of network traffic with phase-type distributions is a powerful means to facilitate the consequent performance evaluation and queuing based analysis. This paper
improves the recently proposed Fixed Hyper-Erlang model (FHE) by introducing an adaptive framework (Adaptive Hyper-Erlang
model, AHE) to determine the crucially performance-sensitive model parameters. The adaptive model fits long-tailed traffic
data set directly with a mixed Erlang distribution in a new divide-and-conquer manner. Compared with the well-known hyperexponential based models and the Fixed Hyper-Erlang model, the Adaptive Hyper-Erlang
model is more flexible and practicable in addition to its accuracy in fitting the tail behavior.
相似文献
Junfeng WangEmail: |
102.
John Stachurski 《Computational Economics》2008,31(2):141-160
This paper studies fitted value iteration for continuous state numerical dynamic programming using nonexpansive function approximators.
A number of approximation schemes are discussed. The main contribution is to provide error bounds for approximate optimal
policies generated by the value iteration algorithm.
相似文献
103.
CAO FeiLong ZHANG YongQuan & XU ZongBen College of Science China Jiliang University Hangzhou China Institute of Information System Sciences Xi’an Jiaotong University Xi’an 《中国科学F辑(英文版)》2009,52(8):1321-1327
Let SFd and Πψ,n,d = { nj=1bjψ(ωj·x+θj) :bj,θj∈R,ωj∈Rd} be the set of periodic and Lebesgue’s square-integrable functions and the set of feedforward neural network (FNN) functions, respectively. Denote by dist (SF d, Πψ,n,d) the deviation of the set SF d from the set Πψ,n,d. A main purpose of this paper is to estimate the deviation. In particular, based on the Fourier transforms and the theory of approximation, a lower estimation for dist (SFd, Πψ,n,d) is proved. That is, dist(SF d, Πψ,n,d) (nlogC2n)1/2 . T... 相似文献
104.
Sampled-data based average consensus with measurement noises: convergence analysis and uncertainty principle 总被引:1,自引:0,他引:1
In this paper, sampled-data based average-consensus control is considered for networks consisting of continuous-time first-order
integrator agents in a noisy distributed communication environment. The impact of the sampling size and the number of network
nodes on the system performances is analyzed. The control input of each agent can only use information measured at the sampling
instants from its neighborhood rather than the complete continuous process, and the measurements of its neighbors’ states
are corrupted by random noises. By probability limit theory and the property of graph Laplacian matrix, it is shown that for
a connected network, the static mean square error between the individual state and the average of the initial states of all
agents can be made arbitrarily small, provided the sampling size is sufficiently small. Furthermore, by properly choosing
the consensus gains, almost sure consensus can be achieved. It is worth pointing out that an uncertainty principle of Gaussian
networks is obtained, which implies that in the case of white Gaussian noises, no matter what the sampling size is, the product
of the steady-state and transient performance indices is always equal to or larger than a constant depending on the noise
intensity, network topology and the number of network nodes. 相似文献
105.
基于相似关系的决策系统的知识获取算法 总被引:1,自引:1,他引:0
陈世联 《计算机工程与科学》2009,31(5)
本文基于一种相似关系下的粗糙集模型,给出了一些基本性质,讨论了该相似关系下的决策系统的知识获取算法。该算法可以避免因数据离散化的处理而产生的信息系统相似度的误差。 相似文献
106.
介绍了一种基于复制结点的消除线路交叉的模型.该模型提出了一个优化问题,就是最小化结点复制的数量.同时提出一个自定义问题——"最大简单共享问题",并证明最小化结点复制的数量与最大共享问题是等价的.证明了最大简单共享问题是NP-hard的,给出了一种简单的贪心算法,并证明该贪心算法的近似度为3.引入一个"最大互斥简单共享问题",该问题是最大简单共享问题的2-近似.将其转化为在一系列图上的完美匹配问题,使该问题可以在多项式时间内得到完美解决.最后,在最大互斥简单共享的基础上,用局部搜索的方法将近似度提高到12/7. 相似文献
107.
108.
109.
110.
Albert A. Groenwold L. F. P. Etman Schalk Kok Derren W. Wood Simon Tosserams 《Structural and Multidisciplinary Optimization》2009,38(4):415-421
Successful gradient-based sequential approximate optimization (SAO) algorithms in simulation-based optimization typically
use convex separable approximations. Convex approximations may however not be very efficient if the true objective function
and/or the constraints are concave. Using diagonal quadratic approximations, we show that non-convex approximations may indeed
require significantly fewer iterations than their convex counterparts. The nonconvex subproblems are solved using an augmented
Lagrangian (AL) strategy, rather than the Falk-dual, which is the norm in SAO based on convex subproblems. The results suggest
that transformation of large-scale optimization problems with only a few constraints to a dual form via convexification need
sometimes not be required, since this may equally well be done using an AL formulation. 相似文献