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Discussion on Normalization Methods of Interval Weights

Received: 16 October 2016     Published: 17 October 2016
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Abstract

This paper is collecting the classic and newly normalization methods, finding deficiency of existing normalization methods for interval weights, and introducing a new normalization methods for interval weights. When we normalize the interval weights, it is very important and necessary to check whether, after normalizing, the location of interval centers as well as the length of interval weights keep the same proportion as those of original interval weights. It is found that, in some newly normalization methods, they violate these goodness criteria. In current work, for interval weights, we propose a new normalization method that reserves both proportions of the distances from interval centers to the origin and of interval lengths, and also eliminates the redundancy from the original given interval weights. This new method can be widely applied in information fusion and decision making in environments with uncertainty.

Published in Science Journal of Applied Mathematics and Statistics (Volume 4, Issue 5)
DOI 10.11648/j.sjams.20160405.19
Page(s) 249-252
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Normalization Methods, Weighted Average, Interval Weights, Information Fusion

References
[1] D. -Q. Li, J. -Y. Wang, and H. -X. Li, Note on “The normalization of interval and fuzzy weights”, Fuzzy Sets and Systems 160 (2009) 2722-2725.
[2] O. Pavlacka, On various approaches to normalization of interval and fuzzy weights, Fuzzy Sets and Systems 243 (2014) 110-130.
[3] P. Sevastjanov, L. Dymova, and P. Bartosiewicz, A new approach to normalization of interval and fuzzy weights, Fuzzy Sets and Systems 198 (2012) 34-45.
[4] Y. -M. Wang, and T. M. S. Elhag, On the normalization of interval and fuzzy weights, Fuzzy Sets and Systems 157 (2006) 2456-2471.
[5] Z. Wang, R. Yang, and K. -S. Leung, Nonlinear integrals and their Applications in Data Mining, World Scientific, 2010.
Cite This Article
  • APA Style

    Yimeng Sui, Zhenyuan Wang. (2016). Discussion on Normalization Methods of Interval Weights. Science Journal of Applied Mathematics and Statistics, 4(5), 249-252. https://doi.org/10.11648/j.sjams.20160405.19

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    ACS Style

    Yimeng Sui; Zhenyuan Wang. Discussion on Normalization Methods of Interval Weights. Sci. J. Appl. Math. Stat. 2016, 4(5), 249-252. doi: 10.11648/j.sjams.20160405.19

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    AMA Style

    Yimeng Sui, Zhenyuan Wang. Discussion on Normalization Methods of Interval Weights. Sci J Appl Math Stat. 2016;4(5):249-252. doi: 10.11648/j.sjams.20160405.19

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  • @article{10.11648/j.sjams.20160405.19,
      author = {Yimeng Sui and Zhenyuan Wang},
      title = {Discussion on Normalization Methods of Interval Weights},
      journal = {Science Journal of Applied Mathematics and Statistics},
      volume = {4},
      number = {5},
      pages = {249-252},
      doi = {10.11648/j.sjams.20160405.19},
      url = {https://doi.org/10.11648/j.sjams.20160405.19},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20160405.19},
      abstract = {This paper is collecting the classic and newly normalization methods, finding deficiency of existing normalization methods for interval weights, and introducing a new normalization methods for interval weights. When we normalize the interval weights, it is very important and necessary to check whether, after normalizing, the location of interval centers as well as the length of interval weights keep the same proportion as those of original interval weights. It is found that, in some newly normalization methods, they violate these goodness criteria. In current work, for interval weights, we propose a new normalization method that reserves both proportions of the distances from interval centers to the origin and of interval lengths, and also eliminates the redundancy from the original given interval weights. This new method can be widely applied in information fusion and decision making in environments with uncertainty.},
     year = {2016}
    }
    

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    AU  - Yimeng Sui
    AU  - Zhenyuan Wang
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    AB  - This paper is collecting the classic and newly normalization methods, finding deficiency of existing normalization methods for interval weights, and introducing a new normalization methods for interval weights. When we normalize the interval weights, it is very important and necessary to check whether, after normalizing, the location of interval centers as well as the length of interval weights keep the same proportion as those of original interval weights. It is found that, in some newly normalization methods, they violate these goodness criteria. In current work, for interval weights, we propose a new normalization method that reserves both proportions of the distances from interval centers to the origin and of interval lengths, and also eliminates the redundancy from the original given interval weights. This new method can be widely applied in information fusion and decision making in environments with uncertainty.
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Author Information
  • Department of Mathematics, University of Nebraska at Omaha, Omaha, United Stated

  • Department of Mathematics, University of Nebraska at Omaha, Omaha, United Stated

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