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Fuzzy And Probability Uncertainty Logics Pdf

fuzzy and probability uncertainty logics pdf

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Fuzzy evidence theory, or fuzzy Dempster-Shafer Theory captures all three types of uncertainty, i. Therefore, it is known as one of the most promising approaches for practical applications. Quantifying the difference between two fuzzy bodies of evidence becomes important when this framework is used in applications.

Possibility Theory,Probability Theory,and Fuzzy Set Theory

Fuzzy logic has been employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. The term fuzzy logic was introduced with the proposal of fuzzy set theory by Lotfi Zadeh. Fuzzy logic has been applied to many fields, from control theory to artificial intelligence. Classical logic only permits conclusions which are either true or false. However, there are also propositions with variable answers, such as one might find when asking a group of people to identify a colour. In such instances, the truth appears as the result of reasoning from inexact or partial knowledge in which the sampled answers are mapped on a spectrum. Humans and animals often operate using fuzzy evaluations in many everyday situations.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Gaines Published Mathematics. Probability theory and fuzzy logic have been presented as quite distinct theoretical foundations for reasoning and decision making in situations of uncertainty.

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. A probabilistic fuzzy logic system for modeling and control Abstract: In this paper, a probabilistic fuzzy logic system PFLS is proposed for the modeling and control problems. Similar to the ordinary fuzzy logic system FLS , the PFLS consists of the fuzzification, inference engine and defuzzification operation to process the fuzzy information. Different to the FLS, it uses the probabilistic modeling method to improve the stochastic modeling capability. By using a three-dimensional membership function MF , the PFLS is able to handle the effect of random noise and stochastic uncertainties existing in the process.

Fuzzy logic

The comparison could be made on very different levels, that is, mathematically, semantically, linguistically, and so on. Fuzzy set theory is not or is no longer a uniquely defined mathematical structure, such as Boolean algebra or dual logic. It is rather a very general family of theories consider, for instance, all the possible operations defined in chapter 3 or the different types of membership functions. In this respect, fuzzy set theory could rather be compared with the different existing theories of multivalued logic. Unable to display preview. Download preview PDF.

The aim of a probabilistic logic also probability logic and probabilistic reasoning is to combine the capacity of probability theory to handle uncertainty with the capacity of deductive logic to exploit structure of formal argument. The result is a richer and more expressive formalism with a broad range of possible application areas. Probabilistic logics attempt to find a natural extension of traditional logic truth tables: the results they define are derived through probabilistic expressions instead. A difficulty with probabilistic logics is that they tend to multiply the computational complexities of their probabilistic and logical components. Other difficulties include the possibility of counter-intuitive results, such as those of Dempster-Shafer theory in evidence-based subjective logic. The need to deal with a broad variety of contexts and issues has led to many different proposals. There are numerous proposals for probabilistic logics.

fuzzy and probability uncertainty logics pdf

Probability theory and fuzzy logic have been presented as quite distinct theoretical foundations for reasoning and decision making in situations of uncertainty.

Fuzzy and Probability Uncertainty Logics

In the proposed framework, belief-dependent concepts such as the strategy with the best expected value are formally derivable in higher-order fuzzy logic for any finite matrix game with rational payoffs. In this paper, we propose a semantics for multi-agent reasoning about uncertain beliefs. Using a suitable fuzzy logic for its representation makes it possible to formalize doxastic reasoning under uncertainty in a rather parsimonious way, which is of particular importance, e.

Хейл, видимо, не догадывается, что она видела его внизу. - Стратмор знает, что я это видел! - Хейл сплюнул.  - Он и меня убьет. Если бы Сьюзан не была парализована страхом, она бы расхохоталась ему в лицо. Она раскусила эту тактику разделяй и властвуй, тактику отставного морского пехотинца.

Вскоре спуск закончился, переключились какие-то шестеренки, и лифт снова начал движение, на этот раз горизонтальное. Сьюзан чувствовала, как кабина набирает скорость, двигаясь в сторону главного здания АНБ. Наконец она остановилась, и дверь открылась. Покашливая, Сьюзан неуверенно шагнула в темный коридор с цементными стенами. Она оказалась в тоннеле, очень узком, с низким потолком. Перед ней, исчезая где-то в темноте, убегали вдаль две желтые линии.

 Мне больно! - задыхаясь, крикнула Сьюзан. Она судорожно ловила ртом воздух, извиваясь в руках Хейла. Он хотел было отпустить ее и броситься к лифту Стратмора, но это было бы чистым безумием: все равно он не знает кода. Кроме того, оказавшись на улице без заложницы, он обречен. Даже его безукоризненный лотос беспомощен перед эскадрильей вертолетов Агентства национальной безопасности. Сьюзан - это единственное, что не позволит Стратмору меня уничтожить.


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