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

Noureen Talpur, Mohd Najib Mohd Salleh, Kashif Hussain

Research output: Chapter in Book/Report/Published conference proceedingConference contributionpeer-review


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.
Original languageEnglish
Title of host publicationIOP Conference Series: Materials Science and Engineering
Subtitle of host publicationInternational Research and Innovation Summit (IRIS2017) 6–7 May 2017, Melaka, Malaysia
PublisherIOP Publishing Ltd.
Publication statusPublished - 14 Aug 2017

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