KNOWLEDGE DISCOVERY FOR CONTROLLING NUCLEAR POWER PLANTS

摘要

Large amounts of data are generated in the investigating and maintaining of nuclear energy. Making full use of this data to gain useful insights into, for example, safety issues, presents a tremendous challenge. It is well known that rough set theory can be applied successfully to rough classification and knowledge discovery. Our work is concerned with finding methods for using rough sets to identify classes in datasets, finding dependencies in relations and discovering rules which are hidden in databases. We believe that these methods can be used to understand and control aspects of nuclear energy generation. So in this paper we illustrate the use of our rough discovery methods by means of a running example using data NPP on controlling for nuclear power plants.