Knowledge representation fuzzy

Sanchez E and Gupta, M.M., eds., Fuzzy Information, Knowledge Representation and Decision Analysis (Pergamon Press, London, 1983). 

Q. Zhang and J.B. Litchfield, Knowledge representation in a grain drier fuzzy logic controller, J. Agric. Eng. Res., 57 269,... 

Boegl, K., K. P. Adlassnig, Y. Hayashi, T. E. Rothenfluh, and H. Leitich, "Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system," Artificial Intelligence in Medicine, 30(1), pp. 1-26,2004. 

Pelaez C.E. and Bowles J.B., (1996) Applying Fuzzy Cognitive-Maps Knowledge-Representation to Failure Mode Effects Analysis , Journal of Information Sciences, Vol. 88, No.l,pp. 177-199. 

The general principle in fuzzy logic is that a reference value Xq is associated with a fuzzy interval dx, and experimental data within an interval of Xq dx are identified as reference data. Since natural, or experimental, data are always inaccurate, and the representation of knowledge is quite like that in fuzzy logic, expert systems have to use fuzzy logic or some techniques similar to fuzzy logic [33]. In a computer system based on the fuzzy logic approach, fuzzy intervals for reference values are defined a priori. 

The Representation of Fuzzy Knowledge, Journal of Cybernetics, volume 4, p57-66, 1974. 

In the history of mathematics, uncertainty was approached in the XVlP century by Pascal and Fermat who introduced the notion of probability. However, probabilities do not allow one to process subjective beliefs nor imprecise or vague knowledge, such as in computer modeling of three-dimensional structure. Subjectivity and imprecision were only considered from 1965, when Zadeh, known for his work in systems theory, introduced the notion of fuzzy set. The concept of fuzziness introduces partial membership to classes, admitting intermediary situations between no and full membership. Zadeh s theory of possibility, introduced in 1977, constitutes a framework allowing for the representation of such uncertain concepts of non-probabilistic nature (9). The concept of fuzzy set allows one to consider imprecision and uncertainty in a single formalism and to quantitatively measure the preference of one hypothesis versus another. Note, however, that Bayesian probabilities could have been used instead. 

The important advantage of the fuzzy logic is in the possibility to represent in formal language a set of if-then rules. These rules allow representation of the human knowledge in the form of conditional sentences. Depending on the number and the type of the connection between the single conditions, there are different methods for calculations. 

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