000 | 03478nam a22005175i 4500 | ||
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001 | 978-3-642-18084-2 | ||
003 | DE-He213 | ||
005 | 20140220083752.0 | ||
007 | cr nn 008mamaa | ||
008 | 110205s2011 gw | s |||| 0|eng d | ||
020 |
_a9783642180842 _9978-3-642-18084-2 |
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024 | 7 |
_a10.1007/978-3-642-18084-2 _2doi |
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050 | 4 | _aQ342 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aPouzols, Federico Montesino. _eauthor. |
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245 | 1 | 0 |
_aMining and Control of Network Traffic by Computational Intelligence _h[electronic resource] / _cby Federico Montesino Pouzols, Diego R. Lopez, Angel Barriga Barros. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011. |
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300 |
_aXVI, 312p. 148 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Computational Intelligence, _x1860-949X ; _v342 |
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505 | 0 | _aInternet Science -- Modeling time series by means of fuzzy inference systems -- Predictive models of network traffic load -- Summarization and analysis of network traffic flow records -- Inference Systems for Network Traffic Control -- Open FPGA-Based Development Platform for Fuzzy Inference Systems. | |
520 | _aAs other complex systems in social and natural sciences as well as in engineering, the Internet is hard to understand from a technical point of view. Packet switched networks defy analytical modeling. The Internet is an outstanding and challenging case because of its fast development, unparalleled heterogeneity and the inherent lack of measurement and monitoring mechanisms in its core conception. This monograph deals with applications of computational intelligence methods, with an emphasis on fuzzy techniques, to a number of current issues in measurement, analysis and control of traffic in the Internet. First, the core building blocks of Internet Science and other related networking aspects are introduced. Then, data mining and control problems are addressed. In the first class two issues are considered: predictive modeling of traffic load as well as summarization of traffic flow measurements. The second class, control, includes active queue management schemes for Internet routers as well as window based end-to-end rate and congestion control. The practical hardware implementation of some of the fuzzy inference systems proposed here is also addressed. While some theoretical developments are described, we favor extensive evaluation of models using real-world data by simulation and experiments. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aInformation systems. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aTelecommunication. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aCommunications Engineering, Networks. |
650 | 2 | 4 | _aInformation Systems Applications (incl.Internet). |
700 | 1 |
_aLopez, Diego R. _eauthor. |
|
700 | 1 |
_aBarros, Angel Barriga. _eauthor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783642180835 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-949X ; _v342 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-642-18084-2 |
912 | _aZDB-2-ENG | ||
999 |
_c107368 _d107368 |