Fuzzy Monte Carlo Simulation using point-cloud-based probability–possibility transformation. (July 2013)

• Record Type: Journal Article
• Authors: Sharif Ullah, A M M; Shamsuzzaman, M
• Other Names: Niazi Muaz A. guest-editor.; Hussain Amir guest-editor.
• Is Part Of: Simulation. Volume 89:Number 7(2013)
• Page Start: 860
• Page End: 875
• Publication Date: 2013-07
• Subjects: Fuzzy Monte Carlo Simulation -- probability–possibility transformation -- fuzzy number -- uncertainty -- imprecision; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549713482174
• Languages: English
• ISSNs: 0037-5497

Abstract

Fuzzy Monte Carlo Simulation (FMCS) uses both the probability density function ( pdf ) and possibility distributions (e.g., fuzzy numbers) to model the uncertainty/imprecision associated with the input parameters and, then, to simulate the uncertainty/imprecision associated with the output parameters. A probability–possibility transformation is needed to transfer the information of a fuzzy number into its equivalent pdf, while performing the simulation. This study deals with an approach of FMCS that uses a point-cloud-based probability–possibility transformation. Let x ( t ), t = 0, 1, …, n, be a set of points that represents some random states of an uncertain/imprecise quantity. The collection of points ( x ( t ), x ( t + i )), t = 0, …, n – i, i ∈ {1, 2, …} is called point-cloud, providing a visual/computational representation of variability, modality, and ranges associated with the quantity. This study identifies the pdf and possibility distribution (fuzzy number) underlying a given point-cloud. Using these distributions, the relationships between the triangular fuzzy number and unimodal pdf (normal/uniform distributions) are identified. Two numerical examples are described elucidating the effectiveness of the proposed transformation. The first example deals with the issue of monitoring a FMCS process. The other example deals with the issue of making a decision by using FMCS.