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Convergence Criteria and Convergence Relations for Sequences of Fuzzy Random Variables

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Fuzzy Systems and Knowledge Discovery (FSKD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3613))

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Abstract

Fuzzy random variable is a measurable map from a probability space to a collection of fuzzy variables. In this paper, we first present several new convergence concepts for sequences of fuzzy random variables, including convergence almost sure, uniform convergence, almost uniform convergence, convergence in mean chance, and convergence in mean chance distribution. Then, we discuss the criteria for convergence almost sure, almost uniform convergence, and convergence in mean chance. Finally, we deal with the relationship among various types of convergence.

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Author information

Authors and Affiliations

  1. College of Mathematics and Computer Science, Hebei University, Baoding, 071002, Hebei, China

    Yan-Kui Liu

  2. School of Information, Renmin University of China, Beijing, 100872, China

    Jinwu Gao

Authors
  1. Yan-Kui Liu
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  2. Jinwu Gao
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

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© 2005 Springer-Verlag Berlin Heidelberg

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Liu, YK., Gao, J. (2005). Convergence Criteria and Convergence Relations for Sequences of Fuzzy Random Variables. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_40

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  • DOI: https://doi.org/10.1007/11539506_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28312-6

  • Online ISBN: 978-3-540-31830-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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