Abstract—In this paper, a new method has been defined to measure the degree of similarity between generalized fuzzy numbers and some properties of the proposed similarity measure have been discussed with examples. The proposed similarity measure is more accurate, robust, free from defect and precise due to the reason that the proposed similarity measure includes minimal parameters to define a generalized fuzzy numbers. This novel similarity measure has many applications in the fields of Clustering, Pattern recognition, Finger-Print matching, Fuzzy risk analysis, Decision- making and Image processing.
Index Terms—Fuzzy numbers, similarity measure, generalized fuzzy numbers.
V. Lakshmana Gomathi Nayagam is with the Department of Mathematics, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India (e-mail: velulakshmanan@nitt.edu).
Geetha Sivaraman is with the PI(WOS-A), DST India, Department of Mathematics, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India (e-mail: geedhasivaraman@ yahoo.com).
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Cite: V. Lakshmana Gomathi Nayagam and Geetha Sivaraman, "A Novel Similarity Measure between Generalized Fuzzy Numbers,"
International Journal of Computer Theory and Engineering vol. 4, no. 3, pp. 448-450, 2012.
