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Comparison of two kinds of fuzzy arithmetic, LR and OFN, applied to fuzzy observation of the cofferdam water level

Czerniak J. M., Dobrosielski W., Angryk R. A.

Computer Science

2013

Vol. 14 (3)

443--457

This paper presents certain important aspect soft he fuzzy logic extension, one of whichis OFN. It includes basic definition soft hat discipline. It also compares fuzzy logic arithmetic with the arithmetic of ordered fuzzy numbers in L-R notation. Computational experiments were based on fuzzy observation of the impounding basin. The results of the study show that there is a connection between the order of OFN number and trend of changes in the environment. The experiment was carried out using computer soft ware developed specially for that purpose.When comparing the arithmetic of fuzzy numbers in L-R notation with the arithmetic of ordered fuzzy number son the ground soft he experiment, it has been concluded that with fuzzy numbers it ispossible to expand the scope of solutions in comparison to fuzzy numbers inclassic form. The symbol of OFN flexibility is the possibility to determine the number that always satisfies the equation A+X=C, regardles soft hevalue of arguments. Operations performed on OFN are less complicated, as the yare performed in the same way regard less the sign of the input data and the irresults are more accuratein the majority of cases.The promising feature of ordered fuzzy numbers is their lack of rapidly growing fuzziness. Authors expect to see implication of that fact in practice in the near future.

Attribute-oriented denazification of fuzzy database tuples with categorical entries

Angryk R. A.

Control and Cybernetics

2009

Vol. 38, no 2

419-453

We are investigating the ability to data mine fuzzy tuples, which are often utilized to represent uncertainty about the registered information. We discuss different aspects of fuzzy databases and comment on practical advantages of the model we utilized in our research. Motivated by a well known technique called Attribute-Oriented Induction, which has been developed for summarization of ordinary relational databases, we propose a new heuristic algorithm, allowing attribute-oriented defuzzification of fuzzy database tuples to the form acceptable for many regular (i.e. atomic values based) data mining algorithms. Significant advantages of our approach to defuzzification of fuzzy database tuples include: (1) its intuitive character of fuzzy tuples' interpretation, (2) a unique capability of incorporating background knowledge, implicitly stored in the fuzzy database models in the form of fuzzy similarity relation, directly into the imprecise data interpretation process, (3) transformation of fuzzy tuples to a format easy to process by regular data mining algorithms, and (4) a good scalability for time-efficient treatment of large datasets containing non-atomic, categorical data entries.