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Computing with Words in Information/Intelligent Systems 1: by Lotfi A. Zadeh (auth.), Prof. Lotfi A. Zadeh, Prof. Janusz

By Lotfi A. Zadeh (auth.), Prof. Lotfi A. Zadeh, Prof. Janusz Kacprzyk (eds.)

The quantity is the 1st one on the planet literature that is a complete, modern account on computing with phrases, a brand new path in commonly perceived clever platforms, proposed and endorsed by way of Professor Zadeh, the founding father of fuzzy units thought and fuzzy good judgment. traditionally, computing was once curious about manipulation of numbers. although, in recent times it turned an increasing number of seen that computing were encompassing not just this, but in addition manipulation on sign, sounds, photos and textual content; the latter facets of computing have gotten a growing number of appropriate. despite the fact that, the conventional manipulation of textual content (e.g., laptop translation, spell checking, etc.) doesn't hide the mainstream of computing with phrases, customarily the illustration and manipulation of propositions from common language describing operations and family. Such difficulties can't be solved via traditional equipment of good judgment, likelihood thought and numerical research. Fuzzy good judgment is proven to be an efficient instrument to address such difficulties.
Computing with phrases may perhaps shape a foundation of a computational conception of perceptions encouraged via a amazing human skill to accomplish a wide selection of initiatives simply at the foundation of obscure and vague details expressed in average language.
In Part 1 foundations of computing with phrases relating to linguistic facets, fuzzy good judgment and approximate reasoning, granularity, calculations on verbal amounts, and basic architectures for the implementation of computing with phrases are presented.

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Additional info for Computing with Words in Information/Intelligent Systems 1: Foundations

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In classical rough set theory, the family of sets, used for the approximation, form an ordinary partition of U. Here nothing has been assumed on the Ii's, although the M(ti)'s are supposed to cover U in practice, and even to make a fuzzy partition of U in a sense to be defmed. ; U j:cons(M(tj),A)>O M(tj) provided that 'V U E (9) U,3 ti E T, IlM(tj)(u) = I; this latter requirement, which expres- ses a strong coverage condition, is necessary in order to have the rightest inclusion. In (Dubois and Prade, 1992b), it has been suggested to use D*(A) and D+(A), for providing linguistic approximations of a fuzzy set A in terms of a given vocabulary.

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23-37. A. (1975), "Calculus of Fuzzy Restrictions," in Fuzzy Sets and Their Applications to Cognitive and Decision Processes, eds. A. S. Fu, M. Shimura, New York: Academic Press, pp. 1-39. A. (1978), "PRUF - a Meaning Representation Language for Natural Languages," Int. J. Man-Machines Studies, 10, pp. 395-460. A. (1979), ''Fuzzy Sets and Information Granularity," in Advances in Fuzzy Set Theory and Applications, eds. M. Ragade and R. Yager, Amsterdam: North-Holland, pp. 3-18. A. (1979), "A Theory of Approximate Reasoning," Machine Intelligence 9, eds.

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