Volume: 03, June 2014, Pages: 256-260 International Journal of Communication and Networking System ISSN: 2278-2427 Integrated Intelligent Research (IIR) 256 Delphi Adapted Fuzzy Associative Memories (DAFAM) as a Multiple Expert System and its Application to Study the Impacts of Climate Change on Environment A.Victor Devadoss 1 , D.Ajay 2 , K.Sudha 3 1 Head & Associate Professor, Department of Mathematics, Loyola College, Chennai-34. 2,3 Ph.D Research Scholar, Department of Mathematics, Loyola College, Chennai-34. Email: hanivictor@ymail.com, dajaypravin@gmail.com, ashu.8788@gmail.com Abstract A new fuzzy technique Delphi Adapted FAM is proposed in this paper and is used to investigate the impacts of climate change on environment. DAFAM functions as a multiple expert system, in that it can be used to combine any number of expert’s views into one relational matrix. The first section of this paper gives an introduction to what is done in the paper and the second section explains the dynamics of FAM. Section three explains the technique DAFAM and in the fourth section DAFAM is adapted to investigate the impacts of climate change on the environment. Section five, the last section derives the conclusion and makes some suggestions. Keywords: Delphi adapted Fuzzy models, Multiple expert system, Fuzzy associative memories, Climate change. 1.Introduction The Delphi method (Dalkey & Helmer, 1963) is a proven tool for collective decision making (Linstone & Turoff, 2002) for a situation in which decision needs to be made by a group of experts who might have divergent views on the topic. This method can also be called a prediction method based ob expert judgment. The Delphi method is characterized by the properties like anonymity, feedback, statistical and convergence. It tries to achieve a consensus among the experts. In order to account for the amount of fuzziness in group decision making Murray, Pipino & Gigch (1985) proposed Fuzzy Delphi method. Since then the method has found many applications. Fuzzy associative memories (FAM) as a model has been applied to analyse problems in which the factors that attribute to the problem can be classified into antecedent and consequent sets and the relationship between them needs to be analysed. In this paper, the novelty of Fuzzy Delphi Method in bringing a consensus is combined with Fuzzy associative memories so that the new technique thus obtained can function as a multiple expert system. 2.Fuzzy Associative Memories (FAM) A fuzzy set is a map μ: X → [0, 1] where X is any set called the domain and [0, 1] the range. That is to every element x  X, μ assigns membership value in the interval [0, 1]. Fuzzy theorists often picture membership functions as two-dimensional graphs with the domain X represented as a one-dimensional axis. The geometry of fuzzy sets involves both domain 1 2 ( , ,... ) n X x x x  and the range [0, 1] of mappings μ: X → [0, 1]. A fuzzy subset equals the unit hyper cube [0,1] n n I  . The fuzzy set is a point in the cube n I . Vertices of the cube n I define a non-fuzzy set. Now within the unit hyper cube [0,1] n n I  we are interested in distance between points, which led to measures of size and fuzziness of a fuzzy set and more fundamentally to a measure. Thus within cube theory directly extends to the continuous case when the space X is a subset of n R . The next step is to consider mappings between fuzzy cubes.