Authors

Uduak Umoh¹, Samuel Udoh¹, Etebong Isong², Regina Akpan¹, and Emmanuel Nyoho¹,
¹University of Uyo, Nigeria and ²Akwa Ibom State University, Nigeria

Abstract

Interval type-2 fuzzy logic systems (IT2FLSs), have recently shown great potential in various applications with dynamic uncertainties. It is believed that additional degree of uncertainty provided by IT2FL allows for better representation of the uncertainty and vagueness present in prediction models. However, determining the parameters of the membership functions of IT2FL is important for providing optimum performance of the system. Particle Swarm Optimization (PSO) has attracted the interest of researchers due to their simplicity, effectiveness and efficiency in solving real-world optimization problems. In this paper, a novel optimal IT2FLS is designed, applied for predicting winning chances in elections. PSO is used as an optimized algorithm to tune the parameter of the primary membership function of the IT2FL to improve the performance and increase the accuracy of the IT2F set. Simulation results show the superiority of the PSO-IT2FL to the similar non-optimal IT2FL system with an increase in the prediction.

Keywords

Type-2 Fuzzy logic system, Particle swarm optimization, optimum fuzzy membership function, politics, democracy, election prediction accuracy