2026-05-05T14:49:32.781Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:3307 2019-06-01T15:12:49Z 3

Modeling Uncertainty and Nonlinearity by Probabilistic Metric Spaces, (Eds. J. Fodor, R. Fuller) Advances in Soft Computing, Intelligent Robotics and Control, Topics in intelligent engineering and informatics 8, Springer 2014 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/3307 E. Pap Many problems occurring in engineering, e.g., robotics and control, require mathematical models which cover uncertainties and nonlinearity. We present here one such model: the theory of probabilistic metric spaces. This theory is based on the idea that, since the value of the distance in measurement is always unprecise and uncertain, the value of the distance have to be a probability distribution function. The theory of fuzzy metric spaces, as another model for uncertainty, is closely related to the theory of probabilistic metric spaces. We consider nonlinear random equations using fixed point methods in probabilistic metric spaces. Probabilistic metric spaces, some constructions methods of triangle functions and some important classes of probabilistic metric spaces as those of Menger, Wald, transformation-generated, are recalled. Based on some additional properties of t-norms the corresponding generalizations of fixed point theorems in probabilistic metric spaces are obtained. bookPart Springer 259 272 10.1007/978-3-319-05945-7 (Eds. J. Fodor, R. Fuller) Advances in Soft Computing, Intelligent Robotics and Control, Topics in intelligent engineering and informatics 8