Title :
Nonlinear system input structure identification based on rough set data analysis
Author :
Ming, Li ; Huaguang, Zhang
Author_Institution :
Sch. of Inf. Sci. & Eng., Northeastern Univ. Shenyang, Liaoning, China
fDate :
6/23/1905 12:00:00 AM
Abstract :
It is important to identify the significant inputs of nonlinear system with large number of possible inputs before any known nonlinear modeling techniques can be applied. We propose an identification method based on rough sets data analysis. The method is driven only by raw data without assuming external structural information such as probability distributions or fuzzy membership functions. The information measure and performance of noise rejection are analyzed. Finally, two examples are given to validate the effectiveness of the method
Keywords :
identification; noise; nonlinear systems; rough set theory; information measure; input structure identification; noise rejection; nonlinear system; raw data; rough set data analysis; Artificial neural networks; Data analysis; Data engineering; Information science; Input variables; Noise measurement; Nonlinear systems; Performance evaluation; Probability distribution; Rough sets;
Conference_Titel :
Fuzzy Systems, 2001. The 10th IEEE International Conference on
Conference_Location :
Melbourne, Vic.
Print_ISBN :
0-7803-7293-X
DOI :
10.1109/FUZZ.2001.1007308
