Wyniki wyszukiwania - BazTech

1

Using the averaged consistency improvment algorithm in interwal conditions

Bednarska U.

Informatyka Teoretyczna i Stosowana

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2008

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R. 8, nr 13

17-27

EN

The aim of this paper is to introduce a new approach to the problem of determining weights in interval Saaty's matrices. An interval matrix is the basic form of a fuzzy matrix and is therefore an important issue, as typical matrices given by experts are not crisp. The proposed method is based on the Consistency Improvement Algorithm [1]. The idea is not to introduce a new formulae for data aggregating, but to present an algorithm that enables us to derive a consistent matrix no matter which judgments are used, as long as they are within the intervals.

PL

Proces oceniania obiektów dokonywany jest zwykle przez więcej niż jednego eksperta. W rezultacie otrzymane macierze porównań parami zadane są w postaci interwałowej. W celu podjęcia decyzji niezbędne jest wyznaczenie jednoznacznej oceny każdego z obiektów. Proponowana w pracy metoda jest modyfikacją Algorytmu Poprawy Spójności, której celem jest zobiektywizowanie procesu wybierania wierszy lub kolumn wykorzystywanych do korekty. Ponadto w pracy zaproponowano nowe podejście do agregacji ocen, polegające na wyborze dowolnej wartości z podanych interwałów, a następnie sprowadzenie otrzymanej macierzy do macierzy spójnej.

2

The granular computing in uncertain identification problems

Orantek P., Burczyński T.

Computer Assisted Mechanics and Engineering Sciences

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2007

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Vol. 14, No. 4

695-704

EN

The paper is devoted to applications of evolutionary algorithms in identification of structures being under the uncertain conditions. Uncertainties can occur in boundary conditions, in material parameters or in geometrical parameters of structures and are modelled by three kinds of granularity: interval mathematics, fuzzy sets and theory of probability. In order to formulate the optimization problem for such a class of problems by means of evolutionary algorithms the chromosomes are considered as interval, fuzzy and random vectors whose genes are represented by: (i) interval numbers, (ii) fuzzy numbers and (iii) random variables, respectively. Description of evolutionary algorithms with granular representation of data is presented in this paper. Various concepts of evolutionary operator such as a crossover and a mutation and methods of selections are described. In order to evaluate the fitness functions the interval, fuzzy and stochastic finite element methods are applied. Several numerical tests and examples of identification of uncertain parameters are presented.

3

Characterization of convex and pointisable interval relations by diagrammatic methods

Kulpa Z., Le T. L.

Machine Graphics and Vision

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2000

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Vol. 9, No. 1/2

221-231

EN

In the paper diagrammatic methods are used to demonstrate equivalence of different characterizations of convex and pointisable interval relations. The diagrammatic tools used are introduced: the MR-diagram for interval space, the W-diagram for representing arrangement interval relations, and the conjunction and lattice diagrams. Two theorems on characterizations on convex and pointisable relation classes are given. The proof of the first theorem has been published by Kulpa elsewhere; the second theorem on pointisable relations is proven in this paper with the help of the diagrammatic tools introduced.