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Expected Value Model and Algorithm for Network Bottleneck Capacity Expansion Under Fuzzy Environment

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Intelligent Computing (ICIC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4113))

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Abstract

This paper considers the capacities of the elements in a set E efficiently so that the total cost for the increment of capacity can be decrease to maximum extent while the final expansion capacity of a given family F of subsets of E is with a given limit bound. The paper supposes the cost w is a fuzzy variable. Network bottleneck capacity expansion problem with fuzzy cost is originally formulated as Expected value model according to some criteria. For solving the fuzzy model efficiently, network bottleneck capacity algorithm, fuzzy simulation, neural network(NN) and genetic algorithm(GA) are integrated to produce a hybrid intelligent algorithm.

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Author information

Authors and Affiliations

  1. College of Management, Wuhan University of Technology, Postfach 430077, Wuhan, Hubei, China

    Yun Wu

  2. Department of Computer Sciences, University of Angers, France

    Zhou Jian

Authors
  1. Yun Wu
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  2. Zhou Jian
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China

    De-Shuang Huang

  2. Carnegie Mellon University,  

    Kang Li

  3. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Stranmillis Road, BT9 5AH, Belfast, UK

    George William Irwin

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© 2006 Springer-Verlag Berlin Heidelberg

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Wu, Y., Jian, Z. (2006). Expected Value Model and Algorithm for Network Bottleneck Capacity Expansion Under Fuzzy Environment. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_82

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  • DOI: https://doi.org/10.1007/11816157_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37271-4

  • Online ISBN: 978-3-540-37273-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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