TY - GEN
A1 - Łęski, Jacek M.
A2 - Rutkowska, Danuta - ed.
A2 - Kacprzyk, Janusz - ed.
A2 - Zadeh, Lotfi A. - ed.
PB - Zielona Góra: Uniwersytet Zielonogórski
N2 - A new learning method tolerant of imprecision is introduced and used in neuro-fuzzy modelling. The proposed method makes it possible to dispose of an intrinsic inconsistency of neuro-fuzzy modelling, where zero-tolerance learning is used to obtain a fuzzy model tolerant of imprecision.
N2 - This new method can be called [epsilon]-insensitive learning, where, in order to fit the fuzzy model to real data, the [epsilon]-insensitive loss function is used. [epsilon]-insensitive learning leads to a model with minimal Vapnik-Chervonenkis dimension, which results in an improved generalization ability of this system. Another advantage of the proposed method is its robustness against outliers.
N2 - This paper introduces two approaches to solving ?-insensitive learning problem. The first approach leads to a quadratic programming problem with bound constraints and one linear equality constraint. The second approach leads to a problem of solving a system of linear inequalities. Two computationally efficient numerical methods for [epsilon]-insensitive learning are proposed. Finally, examples are given to demonstrate the validity of the introduced methods.
L1 - http://www.zbc.uz.zgora.pl/Content/58949/AMCS_2002_12_3_12.pdf
L2 - http://www.zbc.uz.zgora.pl/Content/58949
KW - fuzzy systems
KW - neural networks
KW - tolerant learning
KW - generalization control
KW - robust methods
T1 - Improving the generalization ability of neuro-fuzzy systems by [epsilon]-insensitive learning
UR - http://www.zbc.uz.zgora.pl/dlibra/docmetadata?id=58949
ER -