A Sine Fuzzy information Measure with their Applications in MADM Problems and Pattern Recongnition

Abstract

In the present communication, based on the Sine function, two effective measure are proposed in fuzzy theory, some interesting properties of these measures are analysed. Numerical example is given to show that the information measure of the proposed fuzzy entropy are effective by the comparison of the proposed entropy and the existing entropy. Further, we propose a divergence measure based on Jensen-Sine function which is known as Jensen-Sine divergence measure. It is generalization of J-divergence measure. One of the salient features of this measure is that we can allot the equal weight to each fuzzy set. This makes it specially reasonable for the study of decision making problem. Further, the idea has been generalised from probabilistic to fuzzy divergence measure. At last, the application of proposed Jensen-Sine fuzzy divergence measure is given in strategic decision-making and pattern recognition.