Intelligent Systems for Information Processing: From by B. Bouchon-Meunier, L. Foulloy, Ronald R. Yager

By B. Bouchon-Meunier, L. Foulloy, Ronald R. Yager

Clever structures are required to augment the capacities being made on hand to us via the net and different machine established applied sciences. the speculation essential to aid offering strategies to tough difficulties within the building of clever platforms are mentioned. particularly, cognizance is paid to events within which the on hand info and knowledge will be obscure, doubtful, incomplete or of a linguistic nature. numerous methodologies to control such details are mentioned. between those are the probabilistic, possibilistic, fuzzy, logical, evidential and network-based frameworks.

One function of the publication isn't really to think about those methodologies individually, yet particularly to contemplate how they are often used cooperatively to raised symbolize the multiplicity of modes of knowledge. subject matters within the ebook contain illustration of imperfect wisdom, primary concerns in uncertainty, reasoning, details retrieval, studying and mining, in addition to quite a few applications.

Key Features:

• instruments for building of clever platforms
• Contributions through global prime specialists
• primary matters and functions
• New applied sciences for net looking out
• tools for modeling doubtful info
• destiny instructions in internet applied sciences
• Transversal to tools and domains

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Fuzzy Sets, Uncertainty, and Information. Prentice-Hall, Englewood Cliffs, NJ. , 1995. Fuzzy Sets and Fuzzy Logic. Prentice-Hall, Englewood Cliffs, NJ. , 1993. Representing Uncertain Knowledge. Kluwer, Dordrecht. , 1987. Statistics with Vague Data. Kluwer, Dordrecht. , 1994. Qualitative Reasoning. MIT Press, Cambridge. , 1994. Computation over Fuzzy Quantities. CRC Press, Boca Raton. , 1999. Fuzzy/probability-fractal/smooth. Internat. J. Uncertainty Fuzziness Knowledge-based Systems 7, 363-370.

1962. Subjective probability as the measure of a non-measurable set. , Tarski, A. ), Logic, Methodology and Philosophy of Science. Stanford University Press, Stanford, pp. 319-329. , 1995. Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference. Kluwer, Dordrecht. , 1998. Metamathematics of Fuzzy Logic. Kluwer, Dordrecht. , 1992. Synergetic computation for constraint satisfaction problems involving continuous and fuzzy variables by using Occam. , Umeo, H. ), Transputer/Occam, Proceedings of the Fourth Transputer/Occam International Conference.

Mathematical morphology has been extended in many ways. In the following, we make use of fuzzy morphology, where operations are defined on fuzzy sets (representing spatial entities along with their imprecision) with respect to fuzzy structuring elements. g. [3, 5 , 231). Here, we define dilation and erosion of a fuzzy set p by a structuring element v for all z E S by, respectively: sup,{t[v(y - x)>P(y)Il> Ev(P)(z) = inf,{T[c(v(y - 4 ) > P ( d I } Dv(rU)(z) = where y ranges over the Euclidean space S where the objects are defined, t is a t-norm, and T its associated t-conorm with respect to the complementation c [27].

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