Particle swarm optimization for ANFIS interpretability and accuracy

Abstract

The strength of the adaptive neuro-fuzzy system (ANFIS) involves two contradictory requirements in a common fuzzy modeling problem, i.e. interpretability and accuracy. It is known that simultaneous optimization of accuracy and interpretability will improve performance of the system and avoid over-fitting of data. The objective of this study is the integration of particle swarm optimization (PSO) with ANFIS using modified linguistic and threshold values. This integration is expected to enhance the performance of the ANFIS system in classification problems. PSO is used to tune ANFIS parameters, to improve its classification accuracy. It is also used to find the optimal number of rules and their optimal interpretability. The proposed method has been tested on six standard data sets with different inputs of real and integer data types. The findings indicate that the proposed ANFIS–PSO integration provides a better result for classification, both in interpretability and accuracy.