Kumar Mohit, Stoll Regina, Stoll Norbert
Institute of Occupational and Social Medicine, Faculty of Medicine, University of Rostock, Germany.
IEEE Trans Syst Man Cybern B Cybern. 2006 Aug;36(4):767-80. doi: 10.1109/tsmcb.2006.870625.
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.
本研究关注在存在数据不确定性和建模误差的确定性框架下，可解释的Sugeno型模糊推理系统的自适应学习。作者探索了使用无穷范数估计理论和最小二乘估计来在线学习隶属函数和结论参数，无需任何假设，也不需要关于数据不确定性和建模误差的上界、统计信息及分布的先验知识。数据不确定性、建模误差和时间变化等问题已通过合理的数学方式进行了考量。所提出的用于模糊模型自适应学习的鲁棒方法已通过自适应系统辨识、时间序列预测以及不确定过程估计等实例进行了说明。
