A Novel Learning Rule in Intuitionistic Fuzzy Artificial Neural Network and its Applications to MAGDM Support System

Abstract

In this work, Artificial Neural Network (ANN)-based Multiple Attribute Group Decision Making (MAGDM) models have been provided. These models take into consideration pairs of justifications referred to as weighted geometric pairs, where one component is employed to induce an ordering over the second component, which are intuitionistic fuzzy values, and then aggregated. The recommended strategy of a novel Delta Learning rule named as Robin-Delta Learning rule-based ANN is proposed in this article, where the inputs take the form of intuitionistic fuzzy matrices and are resolved and comparisons are made with the traditional Delta Learning Rule with a numerical illustration. Because it removes the unimportant decision alternatives from the system, the Robin-Delta ANN, which is a groundbreaking contribution in the field of intuitionistic fuzzy ANN, proves to be more effective than the previous methods of MAGDM problems and hence provide a new decision support system in the field of decision making.