Affiliations: [a] Department of Computer Applications, National Institute of Technology, Jamshedpur, Jahrkhand, India | [b] Department of Computer Applications, National Institute of Technology, Jamshedpur, Jahrkhand, India
Correspondence:
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Corresponding author. Harikesh Bahadur Yadav, Department of Computer Applications, National Institute of Technology, Jamshedpur, Jahrkhand, 831014 India. Tel.: +91 0657 2374119; Fax: +91 0657 2373246; E-mail: [email protected].
Abstract: Construction of membership functions from numerical data is very important in various applications of the fuzzy set theory. In last two decade, many methods of membership function generation have been developed. Majority of the methods are application domain dependent and complex. In this paper, a simple method for the construction of membership functions from numerical data is proposed. To validate the proposed method, commonly used and suggested evaluation measures: average error rate, mean magnitude of relative error (MMRE), balanced mean magnitude of relative error (BMMRE), and coefficient of determination (R2), have been taken. The validating results show that proposed method has a higher accuracy than existing methods. The sensitivity analysis has been performed to analyze the impact of input variable on the output variable.
Keywords: Membership function, fuzzy set, fuzzy inference system, k-means clustering, training data
