Interval-Valued Intuitionistic Fuzzy Inference System for Supporting Corporate Financial Decisions

Abstract

Representing the inherent uncertainty in the corporate financial environment is critical for effective decision-making in this domain. This is attributed to the increasing complexity of such an environment. One way in which to address this issue is to represent financial attributes in terms of interval-valued intuitionistic fuzzy sets. In this paper, a novel interval-valued intuitionistic fuzzy inference system (IVIFIS) of the Takagi-Sugeno-Kang type is proposed. To calculate the output of the IVIFIS system, a defuzzification method is developed based on the weighted average of the consequents of if-then rules. To adapt the consequent parameters of the IVIFIS, a gradient algorithm is used. Then, by using two regression problems from the corporate financial domain, the dominance of the system over other state-of-the-art extensions of fuzzy inference systems is experimentally shown.