Publication Date



Technical Report: UTEP-CS-24-11a

To appear in Proceedings of the NAFIPS International Conference on Fuzzy Systems, Soft Computing, and Explainable AI NAFIPS'2024, South Padre Island, Texas, May 27-29, 2024


To understand how different factors and different control strategies will affect a system -- be it a plant, an airplane, etc. -- it is desirable to form an accurate digital model of this system. Such models are known as digital twins. To make a digital twin as accurate as possible, it is desirable to incorporate all available knowledge of the system into this model. In many cases, a significant part of this knowledge comes in terms of expert statements, statements that are often formulated by using imprecise ("fuzzy") words from natural language such as "small", "very possible", etc. To translate such knowledge into precise terms, Zadeh pioneered a technique that he called fuzzy. Fuzzy techniques have many successful applications; however, expert statements are subjective; in contrast to measurement results, they do not come with guaranteed accuracy. In this paper, we show that by using fuzzy techniques, we can translate imprecise expert knowledge into precise probabilistic terms -- thus allowing to combine this knowledge with measurement results into a model with guaranteed accuracy.