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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.

The pipe of samples visualization method as base for evolutionary data discretisation

Czerniak J., Dobrosielski W.

Metody Informatyki Stosowanej

2010

nr 3 (23)

57-67

This article shows the application of the authors’ own method for visualizing multidimensionality, i.e. so called Pipe of Samples, which makes possible to visualize up to 360 dimensions. This approach constituted the base for development of evolutionary discretisation algorithm dedicated for pre-processing of data to be processed using rough sets theory. The study presents operators of crossing, mutation and selection. Structures of the algorithm data have been prepared on the basis of the aforementioned visualization so that each of the achieved individuals described one complete discretisation solution. Hence, in the proposed approach, the population is a set of many complete discretisations of all the attributes. The solution is optimized by means of evolutionary search for the optimum. The study includes results of experiments that compared LDGen adaptation algorithm with other discretisation methods used in rough sets theory. As main components of the article may be regarded such elements like visualisation method, evolutionary data discretisation method including dedicated operators and discussion on the results of experiments.