Identification of Fuzzy Model Based on Rough Sets Theory
LI Ming, ZHANG Hua guang, HE Xi qin (School of Information Science & Engineering, Northeastern University, Shenyang 110006,China)
An identification method for fuzzy model based on rough sets data analysis was proposed. There are two key points in the method. First,the traditional decision table is converted to binary decision table, so the attributes and attribute values in decision table can be dealt at the same time by binary data filter technique. Secondly,the significant attributes and the key attribute values, which are related to the optimized fuzzy partitions of input space,are extracted by using reduction algorithm. So the premiss structures and parameters of fuzzy model are got. The method was illustrated using real data from rock slope practice. The fuzzy model for rock slope stability analysis was built,and the simulation results are reasonable.