Abstract
There are many modern methods for uncertainty modeling developed in last decades. Generally, they are not in conflict with the traditional probabilistic approach since they deal with another (non-probabilistic) types of uncertainties. Moreover, in the solution of real-world problems, the probabilistic and the other types of uncertainties often become apparent simultaneously. Therefore, the synthesis of traditional and modern methods for uncertainty modeling usually provides best results in applications. In this chapter, we present an overview of modern methods based on the fuzzy sets theory (including type-2 fuzzy sets and intuitionistic fuzzy sets), interval analysis, and the Dempster-Shafer theory of evidence (DST). We show the interrelations between these methods and emphasize some problems that impede their applications. We do not intend to present here a comprehensive overview of all modern methods as now they are well presented in numerous books and handbooks. Therefore, in this chapter we present the modern methods for uncertainty modeling only to the extent needed for understanding the applications presented in the following chapters.
Keywords
- Membership Function
- Fuzzy Number
- Interval Arithmetic
- Multiple Criterion Decision Make
- Interval Comparison
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Dymowa, L. (2011). The Methods for Uncertainty Modeling. In: Soft Computing in Economics and Finance. Intelligent Systems Reference Library, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17719-4_3
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