Volume 23 , Issue 1 , PP: 59-72, 2024 | Cite this article as | XML | PDF | Full Length Article
Fuad S. Alduais 1 * , Zahid Khan 2 , Muhammad Waseem 3
Doi: https://doi.org/10.54216/IJNS.230105
The application of neutrosophic statistics provides a novel approach to dealing with uncertain and imprecise data problems. In this study, we present an improved method called neutrosophic Rayleigh exponential weighted moving average chart. The chart is an extension of the traditional model and can be applied in various fields. The proposed scheme is designed to enhance the detection capability of the traditional chart. The key features of the suggested chart are discussed, highlighting its capability to handle vague, indeterminate, and fuzzy data situations. We evaluate the performance of the proposed scheme by analyzing the designated limits and charting parameters for different sample sizes. Moreover, we establish the performance metrics of the chart such as neutrosophic run length ( ) and neutrosophic power curve ( ).Performance metrics demonstrate that the chart is highly sensitive to persistent shifts in the scaling parameter of the neutrosophic Rayleigh distribution. Monte Carlo simulations are conducted to compare the suggested scheme with the existing model. A comparative study indicates that the proposed chart outperforms the competing design, particularly in detecting smaller shifts. Finally, we provide a charting structure for the proposed design using daily average wind speed data, which can be used as a practical implementation guideline for real-world applications.
Neutrosophic probability , Rayleigh model , Control process , Non-normal quality , Estimation , Simulation
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