another method for defuzzification based on regular weighted point

Another Method for Defuzzification Based on Regular Weighted ‎Point‎

‎A new method for the defuzzification of fuzzy numbers is developed in this paper. It is well-known, defuzzification methods allow us to find aggregative crisp numbers or crisp set for fuzzy numbers. But different fuzzy numbers are often converted into one crisp number. In this case the loss of essential information is possible. It may result in inadequate final conclusions, for example, expert...

another method for defuzzification based on regular weighted point

another method for defuzzification based on regular weighted ‎point‎

‎a new method for the defuzzification of fuzzy numbers is developed in this paper. it is well-known, defuzzification methods allow us to find aggregative crisp numbers or crisp set for fuzzy numbers. but different fuzzy numbers are often converted into one crisp number. in this case the loss of essential information is possible. it may result in inadequate final conclusions, for example, expert...

A method for defuzzification based on centroid point

A method for ranking fuzzy numbers based on the centroid point is proposed and some of its desirable properties are studied. Many different methods have been proposed to deal with ranking fuzzy numbers. Constructing ranking indexes based on centroides is an important. But some weaknesses are found in these indexes. The purpose of this article is to give a new ranking index to rank various numbe...

Another‎ Method for Defuzzification Based On Characterization of Fuzzy ‎Numbers

Here we consider approaches to the ranking of fuzzy numbers based upon the idea of associating with a fuzzy number a scalar value, its signal/noise ratios, where the signal and the noise are defined as the middle-point and the spread of each $gamma$-cut of a fuzzy number, respectively. We use the value of a as the weight of the signal/noise ratio of each $gamma$-cut of a fuzzy number to calcula...

Fast Defuzzification Method Based on Centroid Estimation

A new fast defuzzification method is presented. It is based on estimating the centroid position by fitting the fuzzy output shape into one single triangle. In regard to the computational time, this method is comparable to the “Bisector” method and provides better dynamical behavior. This new method was tested to control a second order plant and a benchmark Continuous Stirred Tank Reactor (CSTR).