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Generalization of probability density of random variables

Ziółkowski M.

Scientific Issues of Jan Długosz University in Częstochowa. Mathematics

2009

Vol. 14

163-172

In this paper we present generalization of probability density of random variables. It is obvious that probability density is definite only for absolute continuous variables. However, in many practical applications we need to define the analogous concept also for variables of other types. It can be easily shown that we are able to generalize the concept of density using distributions, especially Dirac’s delta function.

Applying the idea of fusionism in the probability theory

Major M.

Scientific Issues of Jan Długosz University in Częstochowa. Mathematics

2007

Vol. 12

69-76

The solutions presented in this paper may serve as an illustration of the principle of internal integration, know as the idea of fusionism. In the paper we consider some problem. From an urn containing b white balls and c black ones are selected simultaneously some balls. If the balls are of the same colours one of the players wins, otherwise the other player is the winner. For which values of b and c is the game fair?

The granular computing in uncertain identification problems

Orantek P., Burczyński T.

Computer Assisted Mechanics and Engineering Sciences

2007

Vol. 14, No. 4

695-704

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.