Elsevier

Fuzzy Sets and Systems

Volume 87, Issue 1, 1 April 1997, Pages 39-45
Fuzzy Sets and Systems

An efficient algorithm for fuzzy weighted average

https://doi.org/10.1016/S0165-0114(96)00027-9Get rights and content

Abstract

In multisensor intelligent systems, the information fusion plays an important role. Several algorithms have been proposed for the purpose of aggregating imprecise sensory information represented by fuzzy numbers. This paper proposes an efficient algorithm to compute fuzzy weighted average, which turned out to be superior to the previous works by reducing the number of comparisons and arithmetic operations to O(n log n).

References (4)

There are more references available in the full text version of this article.

Cited by (129)

  • A concrete reformulation of fuzzy arithmetic

    2021, Expert Systems with Applications
  • The multi-criteria group decision making methodology using type 2 fuzzy linguistic judgments

    2016, Applied Soft Computing Journal
    Citation Excerpt :

    Therefore, one may use either approach to obtain the globally aggregated IT2FS ad libitum. We suggest that using the EFWA [55] accompanied by the algorithm [16] is more easily in programming the calculation. New Product Development (NPD) is largely uncertain, because of competition accompanied with modern technology and market changes.

  • Extended gradual interval (EGI) arithmetic and its application to gradual weighted averages

    2014, Fuzzy Sets and Systems
    Citation Excerpt :

    In this context, fuzzy interval identification is viewed as an interval regression problem for input–output data [3,9,15,74,75]. From a practical point of view and to illustrate the gradual computation and approximation concept, we use the common fuzzy weighted average (FWA) [11,17,25,38,46,52]. We only view the weighted average as a potential application of the proposed gradual operators.

View all citing articles on Scopus
1

Supported in part by KOSEF (#951-0903-047-z).

View full text