Title :
A self-learning fuzzy modeling approach with its application to EEG time-series prediction problem
Author :
Jianhua, Zhang ; Xingyu, Wang
Author_Institution :
Dept. of Autom., East China Univ. of Sci. & Technol., Shanghai
Abstract :
A self-learning fuzzy modeling approach based on TSK model is proposed in this paper. Based on the input-output training data, the fuzzy system optimizes the linear parameters in the THEN part of the fuzzy rules using the steady-state Kalman filter and the membership function parameters in the IF part by using supervised Gaussian learning rule. The application to EEG time-series prediction has demonstrated the practical effectiveness of the approach proposed.
Keywords :
Kalman filters; electroencephalography; fuzzy set theory; medical signal processing; self-adjusting systems; time series; EEG time-series prediction problem; TSK model; fuzzy rules; input-output training data; linear parameters; membership function; self-learning fuzzy modeling; steady-state Kalman filter; supervised Gaussian learning rule; Algorithm design and analysis; Automation; Brain modeling; Electroencephalography; Electronic mail; Fuzzy systems; Kalman filters; Predictive models; Steady-state; Training data; EEG analysis; Fuzzy modeling; Hybrid learning algorithm; Kalman filter;
Conference_Titel :
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location :
Kunming
Print_ISBN :
978-7-900719-70-6
Electronic_ISBN :
978-7-900719-70-6
DOI :
10.1109/CHICC.2008.4605062
