Information Processing Based on Mixed - Classical and Fuzzy - Data Models
Orsolya Takács and Annamária R. Várkonyi-Kóczy
Dept. of Measurement and Information Systems
Budapest University of Technology and Economics, Magyar Tudósok körùtia 2. E444, H-1117, Budapest, Hungary
Received:October 20, 2000Accepted:December 10, 2000Published:January 20, 2001
Keywords:uncertainty representation, uncertainty measure, non-liner systems, fuzzy data model, probabilty theory
The model used to represent information during information processing could affect achievable accuracy and could determine the usability of different calculation methods. The data model must also be able to represent uncertainty and inaccuracy both of input data and results. The two most popular data models for representation of uncertain data is the "classical", probability based, and the recently introduced fuzzy data models. Both data models have their own calculation and data processing methods, but with the increasing complexity of calculation problems, a method for the mixed use of these data models is be needed. This paper deals with possible solutions for information processing based on mixed data models and examines the different conversion methods between fuzzy and probability theory based data models.
Cite this article as:O. Takács and A. Várkonyi-Kóczy, “Information Processing Based on Mixed - Classical and Fuzzy - Data Models,” J. Adv. Comput. Intell. Intell. Inform., Vol.5 No.1, pp. 44-50, 2001.Data files: