Ambiguity has an important part in the contrary observations around peripheral world. Entropy is imperative for measuring uncertain information which was first introduced by Shannon (1948) to measure the uncertain degree of randomness in a probability distribution. Fuzzy information measures have been applied widely in the area of decision making. Jensen–Shannon divergence is a useful distance measure in the probability distribution space. The present communication we propose a way of measuring the difference between two fuzzy sets by means of a function, called divergence. In addition, study of their detailed properties for its validity is also discussed. The applications of these newly developed fuzzy divergence measure have been provided to the optimal decision making based on the weights of alternatives. Numerical verification has been illustrated to demonstrate the proposed method for solving optimal decision-making problem under fuzzy environment.
展开▼
