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This paper presents two novel and useful defuzzificationmethods for fuzzy set outputs. Two algorithms based on root mean square (RMS) to obtain a new defuzzification procedure are proposed. In order to validate the efficacy of the proposed algorithms the results are compared with the existing defuzzification methods such as weighted average, centroid (COG) and mean of maxima. The satisfaction of a set of essential constraints is also dealt with which motivates a step towards rational defuzzification algorithm. These new methods RMS 1 and RMS 2 stand on par with the most commonly used COG method in every respect. In addition, the value obtained by RMS 2 is always higher and hence when a higher value is needed or desirable this can be employed advantageously.

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The goal of this paper is to present new and different quantifiers for ordered weighted aggregation and illustrate their applicability by a real-life example. The role of these operators in the formulation of multicriteria decision making functions, using the concept of quantifier guided aggregation, is also discussed.

The main emphasis of this paper is on fuzzy linguistic hedging, used to modify membership functions. This paper investigates the issues of obtaining new definitions for hedges which exceed the traditional definitions given by Zadeh (and others), particularly seeing that the effect of applying these hedges does not cross beyond the reasonable limits of membership values[0,1]and is still meaningful from the point of view of magnitude of membership value and hence be really effective for an application. Some of the most commonly used hedges are presented, these hedges are very, positively, negatively, slightly more, and slightly less. The effects of these hedges on numeric examples are charted.