Deterministic approach to robust adaptive learning of fuzzy models.
This study is concerned with the adaptive learning of an interpretable Sugeno-type fuzzy inference system, in a deterministic framework, in the presence of data uncertainties and modeling errors. The authors explore the use of Hinfinity estimation theory and least squares estimation for online learning of membership functions and consequent parameters without making any assumption and requiring a priori knowledge of upper bounds, statistics, and distribution of data uncertainties and modeling errors. The issues of data uncertainties, modeling errors, and time variations have been considered mathematically in a sensible way. The proposed robust approach to the adaptive learning of fuzzy models has been illustrated through the examples of adaptive system identification, time-series prediction, and estimation of an uncertain process.
Kumar
Mohit
M
Institute of Occupational and Social Medicine, Faculty of Medicine, University of Rostock, Germany. mohit.kumar@uni-rostock.de
Stoll
Regina
R
Stoll
Norbert
N
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005246:Feedback; D017143:Fuzzy Logic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*