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International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing

RSFDGrC 2009: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing pp 261–268Cite as

Analysis of Ergodicity of a Fuzzy Possibilistic Markov Model

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5908))

Abstract

Performing ergodicity analysis is essential to study the long realization of a model. In this paper we analyze the ergodicity, i.e.,the existence of the limiting fuzzy transition possibility matrix with identical rows for the fuzzy transition possibility matrix \(\tilde{H}\) of a fuzzy possibilistic Markov model which contains a state j such that the transition from every state to the state j is a sure event.

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References

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Author information

Authors and Affiliations

  1. Department of Mathematics, SSN College of Engineering, Kalavakkam, Chennai, 603 110, India

    B. Praba, R. Sujatha & V. Hilda Christy Gnanam

Authors
  1. B. Praba
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  2. R. Sujatha
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  3. V. Hilda Christy Gnanam
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Aided Sciences, Kyushu Institute of Technology, 804-8550, Tobata, Kitakyushu, Japan

    Hiroshi Sakai

  2. Department of Pure Mathematics, University of Calcutta, 35 Ballygunge Circular Road, 700019, Kolkata, India

    Mihir Kumar Chakraborty

  3. Information Technology Department, University of Cairo, 5 Ahmed Zewal St. Orman, Giza, Egypt

    Aboul Ella Hassanien

  4. University of Warsaw & Infobright Inc., Poland

    Dominik Ślęzak

  5. University of Electronic Science and Technology of China, Chengdu, China

    William Zhu

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© 2009 Springer-Verlag Berlin Heidelberg

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Praba, B., Sujatha, R., Gnanam, V.H.C. (2009). Analysis of Ergodicity of a Fuzzy Possibilistic Markov Model. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_32

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  • DOI: https://doi.org/10.1007/978-3-642-10646-0_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10645-3

  • Online ISBN: 978-3-642-10646-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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