Abstract
In this paper there is presented the computational study of fuzzy weighted averaging of data in the presence of non-stationary noise. There is proposed a new method, which is an extension of Weighted Averaging using Criterion Function Minimization (WACFM). In the method the weighting coefficients are fuzzy numbers instead of classical real numbers. The determining of these coefficients requires the extending of WACFM method for certain types of fuzzy numbers. In the presented case there is made an assumption of the triangle membership function for fuzzy coefficients. The performance of presented method is experimentally evaluated and compared with the traditional arithmetic averaging as well as Weighted Averaging using Criterion Function Minimization (WACFM) for the ECG signal.
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Momot, A., Momot, M. (2009). Fuzzy Weighted Averaging Using Criterion Function Minimization. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_28
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DOI: https://doi.org/10.1007/978-3-642-00563-3_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00562-6
Online ISBN: 978-3-642-00563-3
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