A Centroid-based Ranking Method of Trapezoidal Intuitionistic Fuzzy Numbers and Its Application to MCDM ProblemsDas, Satyajit; Guha, Debashree
2016 Fuzzy Information and Engineering
doi: 10.1016/j.fiae.2016.03.004
AbstractThe objective of this paper is to introduce a novel method to compare trapezoidal intuitionistic fuzzy numbers (TrIFNs). Till now little research has been done regarding the ranking of TrIFNs. This paper first reviews the existing ranking methods and shows their drawbacks by using several examples. In order to overcome the drawbacks of the existing methods, a new ranking method of TrIFNs is developed by utilizing the concept of centroid point. For this purpose, centroid point for TrIFN is also defined. The rationality validation of the proposed centroid formulae is proved. Further, the ranking method is applied to a multi-criteria decision making (MCDM) problem in which the ratings of the alternatives on criteria are expressed with TrIFNs. Finally, the effectiveness and applicability of the proposed ranking method are illustrated with an aerospace research organization center selection example. This article has also justified the proposed approach by analyzing a comparative study.
Boundary and Interior Nodes in a Fuzzy Graph Using Sum DistanceTom, Mini; Sunitha, M. S.
2016 Fuzzy Information and Engineering
doi: 10.1016/j.fiae.2015.07.001
AbstractIn this paper, the concepts of boundary nodes and interior ones are introduced in a fuzzy graph based on sum distance with relationship among boundary nodes, interior nodes, fuzzy cutnodes and complete nodes. It is observed that fuzzy cutnodes can be boundary nodes and there are nodes in , neither boundary nodes nor interior ones. It is verified that a complete node need not be a boundary one and a node which is a boundary one of all other nodes need not be complete. In fuzzy trees, it is observed that fuzzy end nodes need not be boundary ones and vice versa. It is verified that in a complete fuzzy graph there exist at most one node which is not a boundary one. Also boundary nodes of a self centered fuzzy cycle are identified together with interior nodes of a complete fuzzy graph.
Artificial Neural Network Based Model for Forecasting of Inflation in IndiaThakur, Gour Sundar Mitra; Bhattacharyya, Rupak; Mondal, Seema Sarkar
2016 Fuzzy Information and Engineering
doi: 10.1016/j.fiae.2016.03.005
AbstractInflation can be attributed to both microeconomic and macroeconomic factors which influence the stability of the economy of any nation. With the raising of recession at the end of the year 2008, world communities started paying much contemplation on inflation and put enormous hard work to predict it accurately. Prediction of inflation is not a simple task. Moreover, the behavior of inflation is so complex and uncertain that both economists and statisticians have been striving to model and forecast inflation in an accurate way. As a result, many researchers have proposed inflation forecasting models based on different methods; however the accuracy is always being a major constraint. In this paper, we have analyzed the historical monthly economic data of India between January 2000 and December 2012 and constructed an inflation forecasting model based on feed forward back propagation neural network. Initially some critical factors that can considerably influence the inflation of India have been identified, then an efficient artificial neural network (ANN) model has been proposed to forecast the inflation. Accuracy of the model is proved to be satisfactory when compared with the forecasting of some well-known agencies.