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 decisionmaking 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 intervalvalued intuitionistic fuzzy sets. In this paper, a novel intervalvalued 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.

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

“…For developing this kind of system, the Mamdani-type FIS are widely used [52,53]. Fuzzy decision support systems are used in different knowledge areas such as Medicine [53,54,55,56,57], Agriculture [58,59], Financial [60,61,62,63,64], Construction [65], Education [66], and more.…”

Defuzzification of Non-Linear Pentagonal Intuitionistic Fuzzy Numbers and Application in the Minimum Spanning Tree Problem

“…The authors only used three components of a triangular intuitionistic fuzzy number which these components are used to determine. Hajek and Olej [17] investigated an interval-valued intuitionistic fuzzy inference system that is similar to the Takagi-Sugeno-Kang type system. The authors proposed a defuzzification method to obtain a crisp output from the evaluated system.…”

“…The authors used the difference between the membership function and non-membership function as the weights of the defuzzification methods. In the aforementioned studies [8][9][10][11][12][13][14][15][16][17]21,22], scientists preferred using weighted-averaging-based defuzzification methods. In actual applications, the authors chose different weights, such as membership values, non-membership values, hesitancy values, accuracy values, the difference between sides of (α − β) cuts, and possibility values.…”

Information Technology for Designing Rule bases of Fuzzy Systems using Ant Colony Optimization

“…The fuzzification unit determines the membership degree for the numerical values of all n variables of the vector X to the corresponding input LTs of the considered fuzzy system [30]. The fuzzy inference engine, in turn, based on fuzzified signals and data received from the rule base, sequentially performs aggregation, activation and accumulation operations [31]. The rule base consists of a set of rules composed of defined antecedents and consequents.…”

“…where A1, A2, A3, A4, B1, B2, B3, B4 are certain linguistic terms of FS inputs and outputs. The defuzzification unit transforms the consolidated fuzzy inference into a crisp numerical signal for each FS output variable [31].…”