Article: An artificial intelligence methodology for enhancing Sugeno defuzzification method Journal: International Journal of Fuzzy Computation and Modelling (IJFCM) 2020 Vol.3 No.2 pp.95 - 119 Abstract: This research is focused on investigating principal components analysis (PCA) for defuzzification which estimates singleton fuzzy values in a subjective way for Sugeno defuzzification method. This work goes beyond the approach of Sugeno defuzzification method to defuzzification where singleton fuzzy values are considered to be objective. The new artificial intelligence methodology is proposed for improving Sugeno defuzzification method by directly integrating it with a principal component analyser, a fuzzy inference engine, a knowledge base, and a user interface. For the chosen datasets, the artificial intelligence methodology improved accuracy by considering the difference between the predicted value and actual value. The improvements were up to 98%, 98%, 71%, and 95% for the Card Payment, non-communicable disease, communicable disease and Manas Prakriti respectively, while Sugeno defuzzification method shows significantly low accuracy by considering the difference between predicted value and actual value. Inderscience Publishers - linking academia, business and industry through research

Title: An artificial intelligence methodology for enhancing Sugeno defuzzification method

Authors: Devaradura Somindra Kalana Mendis; Hemali Uditha Wijewardane Ratnayake; Asoka Somabandu Karunananda; Samaratunga Wedage Udaya Samaratunga

Addresses: Department of Information Technology, Advanced Technological Institute, Dehiwala, 10350, Sri Lanka ' Department of Electrical and Computer Engineering, Open University of Sri Lanka, Nawala, 11222, Sri Lanka ' Department of Computational Mathematics, University of Moratuwa, Moratuwa, 10400, Sri Lanka ' Gampaha Wickramarachi Ayurvedic Institute, University of Kelaniya, Yakkala, 11600, Sri Lanka

Abstract: This research is focused on investigating principal components analysis (PCA) for defuzzification which estimates singleton fuzzy values in a subjective way for Sugeno defuzzification method. This work goes beyond the approach of Sugeno defuzzification method to defuzzification where singleton fuzzy values are considered to be objective. The new artificial intelligence methodology is proposed for improving Sugeno defuzzification method by directly integrating it with a principal component analyser, a fuzzy inference engine, a knowledge base, and a user interface. For the chosen datasets, the artificial intelligence methodology improved accuracy by considering the difference between the predicted value and actual value. The improvements were up to 98%, 98%, 71%, and 95% for the Card Payment, non-communicable disease, communicable disease and Manas Prakriti respectively, while Sugeno defuzzification method shows significantly low accuracy by considering the difference between predicted value and actual value.

Keywords: Sugeno defuzzification method; artificial intelligence methodology; knowledge modelling; PCA; principal components analysis; Ayurveda medicine.

DOI: 10.1504/IJFCM.2020.110190

International Journal of Fuzzy Computation and Modelling, 2020 Vol.3 No.2, pp.95 - 119

Accepted: 20 Jan 2020
Published online: 08 Oct 2020 *

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Title: An artificial intelligence methodology for enhancing Sugeno defuzzification method

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