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

fYear

2008

fDate

16-18 July 2008

Firstpage

218

Lastpage

221

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/CHICC.2008.4605062

Filename

4605062