سال انتشار: ۱۳۸۶
محل انتشار: اولین کنگره مشترک سیستم های فازی و سیستم های هوشمند
تعداد صفحات: ۶
M Sabeti – Department of Computer Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran
M. H. Sadreddini – Department of Computer Science and Engineering, School of Engineering, Shiraz University, Shiraz, Iran
G. W. Price – School of Psychiatry and Clinical Neuroscience, University of Western Australia, Perth, Australia
In this paper, EEG signals of twenty schizophrenic patients and twenty age-matched healthy subjects are analyzed with objective of classification two groups. In this case, Fuzzy Accuracy-based Classifier System (F-XCS) is used to automatically generate fuzzy if-then rules for discrimination healthy and schizophrenic subjects. Several features including AR model coefficients, band power and fractal dimension are extracted from EEG signals. First, the F-XCS is applied where a randomly generated initial population of fuzzy if-then rules is evolved by typical genetic operations such as selection, crossover and mutation. Second, a heuristic procedure for improving the performance of FXCS is applied and result of adding this heuristic procedure is analyzed. The motivation behind this approach is that FXCS will be capable of generating compact, high performance rule sets which are general and accurate.