Improving the Generalization Ability of Neuro-Fuzzy Systems by e-Insensitive Learning

• Autorzy: Łęski J.
• Języki publikacji: EN

Abstrakty

EN

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. This new method can be called e-insensitive learning, where, in order to fit the fuzzy model to real data, the e-insensitive loss function is used. e-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. This paper introduces two approaches to solving e-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 e-insensitive learning are proposed. Finally, examples are given to demonstrate the validity of the introduced methods.

Słowa kluczowe

EN

fuzzy systems; neural networks; tolerant learning; generalization control; robust methods

PL

informatyka

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2002
• Tom: Vol. 12, no 3
• Strony: 437--447
• Opis fizyczny: Bibliogr., rys., tab., wykr.

Twórcy

autor

Łęski J.

• Institute of Electronics Silesian University of Technology Akademicka 16, 44-100 Gliwice, Poland,