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第116行： − + − 自相似对于计算机网络的设计有着重要的意义，因为典型的网络流量具有自相似的特性。例如，在电信流量工程中，分组交换数据流量模式似乎在统计上是自相似的。这种性质意味着使用泊松分布的简单模型是不准确的，而没有考虑自相似性的网络很可能以意想不到的方式运行。+ − + − Similarly, stock market movements are described as displaying self-affinity, i.e. they appear self-similar when transformed via an appropriate affine transformation for the level of detail being shown.<ref>{{cite magazine | url=https://www.scientificamerican.com/article/multifractals-explain-wall-street/ | title=How Fractals Can Explain What's Wrong with Wall Street | author=Benoit Mandelbrot | magazine=Scientific American|+ − − 类似地，股票市场的运动被描述为表现出自我亲和力，即。当通过适当的仿射变换显示细节的水平时，它们看起来自我相似。 不同的 https://www.scientificamerican.com/article/multifractals-Explain-Wall-Street/ 可以解释什么是华尔街的错误 | 作者本华·曼德博 | 科学美国人 | − − date=February 1999| authorlink=Benoit Mandelbrot}}</ref> [[Andrew Lo]] describes stock market log return self-similarity in [[econometrics]].<ref>Campbell, Lo and MacKinlay (1991) "[[Econometrics]] of Financial Markets ", Princeton University Press! {{ISBN|978-0691043012}}</ref> − − 类似地，股票市场的运动被描述为显示自亲和性，也就是说，当根据显示的细节水平通过适当的仿射变换进行转换时，它们显得自相似。罗安儒描述了计量经济学中股票市场的对数回报自相似性。
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曼德勃罗集在米约维奇点附近也具有自相似性。
曼德勃罗集在米约维奇点附近也具有自相似性。
Self-similarity has important consequences for the design of computer networks, as typical network traffic has self-similar properties. For example, in [[teletraffic engineering]], [[packet switched]] data traffic patterns seem to be statistically self-similar.<ref>{{cite journal|last1=Leland|first1=W.E.|last2=Taqqu|first2=M.S.|last3=Willinger|first3=W.|last4=Wilson|first4=D.V.|display-authors=2|title=On the self-similar nature of Ethernet traffic (extended version)|journal=IEEE/ACM Transactions on Networking|date=January 1995|volume=2|issue=1|pages=1–15|doi=10.1109/90.282603|url=http://ccr.sigcomm.org/archive/1995/jan95/ccr-9501-leland.pdf}}</ref> This property means that simple models using a [[Poisson distribution]] are inaccurate, and networks designed without taking self-similarity into account are likely to function in unexpected ways.
Self-similarity has important consequences for the design of computer networks, as typical network traffic has self-similar properties. For example, in [[teletraffic engineering]], [[packet switched]] data traffic patterns seem to be statistically self-similar.<ref name=":1">{{cite journal|last1=Leland|first1=W.E.|last2=Taqqu|first2=M.S.|last3=Willinger|first3=W.|last4=Wilson|first4=D.V.|display-authors=2|title=On the self-similar nature of Ethernet traffic (extended version)|journal=IEEE/ACM Transactions on Networking|date=January 1995|volume=2|issue=1|pages=1–15|doi=10.1109/90.282603|url=http://ccr.sigcomm.org/archive/1995/jan95/ccr-9501-leland.pdf}}</ref> This property means that simple models using a [[Poisson distribution]] are inaccurate, and networks designed without taking self-similarity into account are likely to function in unexpected ways.
Self-similarity has important consequences for the design of computer networks, as typical network traffic has self-similar properties. For example, in teletraffic engineering, packet switched data traffic patterns seem to be statistically self-similar. This property means that simple models using a Poisson distribution are inaccurate, and networks designed without taking self-similarity into account are likely to function in unexpected ways.
Self-similarity has important consequences for the design of computer networks, as typical network traffic has self-similar properties. For example, in teletraffic engineering, packet switched data traffic patterns seem to be statistically self-similar. This property means that simple models using a Poisson distribution are inaccurate, and networks designed without taking self-similarity into account are likely to function in unexpected ways.
自相似对于计算机网络的设计有着重要的意义，因为典型的网络流量具有自相似的特性。例如，在电信流量工程中，分组交换数据流量模式似乎在统计上是自相似的<ref name=":1" />。这种性质意味着使用泊松分布的简单模型是不准确的，而没有考虑自相似性的网络很可能以意想不到的方式运行。
Similarly, [[stock market]] movements are described as displaying [[self-affinity]], i.e. they appear self-similar when transformed via an appropriate [[affine transformation]] for the level of detail being shown.<ref>{{cite magazine | url=https://www.scientificamerican.com/article/multifractals-explain-wall-street/ | title=How Fractals Can Explain What's Wrong with Wall Street | author=Benoit Mandelbrot | magazine=Scientific American|
Similarly, [[stock market]] movements are described as displaying [[self-affinity]], i.e. they appear self-similar when transformed via an appropriate [[affine transformation]] for the level of detail being shown.<ref name=":2">Peitgen, et al (1991), p.2-3.</ref> [[Andrew Lo]] describes stock market log return self-similarity in [[econometrics]].<ref name=":3">Campbell, Lo and MacKinlay (1991) "[[Econometrics]] of Financial Markets ", Princeton University Press! {{ISBN|978-0691043012}}</ref>
类似地，人们在描述股票市场时认为其波动具有自仿射性，也就是说，当根据显示的细节程度，通过适当的仿射变换进行转换时，它们显示出自相似性<ref name=":2" />。 罗安儒描述了计量经济学中股票市场的对数回报自相似性<ref name=":3" />。
[[Finite subdivision rules]] are a powerful technique for building self-similar sets, including the [[Cantor set]] and the [[Sierpinski triangle]].
[[Finite subdivision rules]] are a powerful technique for building self-similar sets, including the [[Cantor set]] and the [[Sierpinski triangle]].