An investigation of membership functions on performance of ANFIS for solving classification problems

International Research and Innovation Summit 2017

Talpur, Noureen^1 ; Salleh, Mohd Najib Mohd^1 ; Hussain, Kashif^1

Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia^1

关键词: Adaptive neuro fuzzy inference systems (ANFIS);    ANFIS model;    Degree of accuracy;    Expert knowledge;    Gaussian membership function;    Machine learning techniques;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/226/1/012103/pdf
DOI  :  10.1088/1757-899X/226/1/012103

来源: IOP

【 摘 要 】

Adaptive neuro-fuzzy inference system (ANFIS) is one of the efficient machine learning techniques, which has been successfully employed in wide variety of applications. The performance of ANFIS depends on the selection of the number and shape of membership functions as these two factors influence the most on computational complexity and accuracy of the designed ANFIS-based model. Mostly, an expert knowledge is required in this regard. However, there is an immense need of an investigative study for helping researchers make better decision on the number and shape of membership functions for thier ANFIS models. Hence, this study examines the role of four popular shapes of membership functions on the performance of ANFIS while solving various classification problems. According to experiments, Gaussian membership function demonstrated higher degree of accuracy with lesser computational complexity as compared to the counterparts.