246 Int. J. Applied Systemic Studies, Vol. 8, No. 3, 2018 Copyright © 2018 Inderscience Enterprises Ltd. On generalised intutionistic fuzzy divergence Anjali Munde Amity College of Commerce and Finance, Amity University, Uttar Pradesh, India Email: anjalidhiman2006@gmail.com Abstract: Atanassov (1986) defined the notion of intuitionistic fuzzy sets (IFS), which is a generalisation of the concept of fuzzy sets, introduced by the Zadeh (1965). Decision makers may not be able to accurately express their view for the problem as they may not possess a precise or sufficient knowledge of the problem or the decision makers are unable to discriminate explicitly the degree to which one alternative are better than others in such cases, the decision maker may provide their preferences for alternatives to certain degree, but it is possible that they are not so sure about it. Thus, it is very suitable to express the decision maker preference values with the use of fuzzy/intuitionistic fuzzy values rather than exact numerical values or linguistic variables (Szmidt and Kacprzyk, 2001, 1997, 2000). In the present communication, generalised measures of intutionistic fuzzy divergence with the proof of their validity are introduced. Keywords: fuzzy sets; fuzzy entropy; intuitionistic fuzzy set; intuitionistic fuzzy divergence. Reference to this paper should be made as follows: Munde, A. (2018) ‘On generalised intutionistic fuzzy divergence’, Int. J. Applied Systemic Studies, Vol. 8, No. 3, pp.246–254. Biographical notes: Anjali Munde works at Amity College of Commerce and Finance, Amity University Uttar Pradesh. Her areas of interest are fuzzy information measures, decision making and coding theory. She has published various research papers in international journals of high repute. 1 Introduction Although mathematics is the learning of a set of objects, we would unite it to numerous quantitative measures outlined above the set. Quantitative measure with every entity and the variance or divergence amongst any two entities are the two essential measures. Shannon (1948) has explained entropy through probability distribution in a series of probability distributions in information theory. Kullback and Leibler (1951) introduced the measure of divergence which is a measure of the degree to which the expected probability distribution diverges from the exact one. Similar to probability theory Zadeh