Title
A Convolution-Based Distance Measure For Fuzzy Singletons And Its Application In A Pattern Recognition Problem
Abstract
A new method to measure the distance between fuzzy singletons (FSNs) is presented. It first fuzzifies a crisp number to a generalized trapezoidal fuzzy number (GTFN) using the Mamdani fuzzification method. It then treats an FSN as an impulse signal and transforms the FSN into a new GTFN by convoluting it with the original GTFN. In so doing, an existing distance measure for GTFNs can be used to measure distance between FSNs. It is shown that the new measure offers a desirable behavior over the Euclidean and weighted distance measures in the following sense: Under the new measure, the distance between two FSNs is larger when they are in different GTFNs, and smaller when they are in the same GTFN. The advantage of the new measure is demonstrated on a fuzzy forecasting trading system over two different real stock markets, which provides better predictions with larger profits than those obtained using the Euclidean distance measure for the same system.
Year
DOI
Venue
2021
10.3233/ICA-200629
INTEGRATED COMPUTER-AIDED ENGINEERING
Keywords
DocType
Volume
Distance of fuzzy entities, fuzzy singleton (FSN), similarity measure, generalized trapezoidal fuzzy numbers (GTFN), convolution, pattern recognition, forecasting, stock market
Journal
28
Issue
ISSN
Citations 
1
1069-2509
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Rodrigo Naranjo100.34
Matilde Santos214324.39
Luis Garmendia300.34