Struktura obiektu

Autor:

Łęski, Jacek M.

Współtwórca:

Rutkowska, Danuta - ed. ; Kacprzyk, Janusz - ed. ; Zadeh, Lotfi A. - ed.

Tytuł:

Improving the generalization ability of neuro-fuzzy systems by [epsilon]-insensitive learning

Podtytuł:

Computing with Words and Perceptions

Tytuł publikacji grupowej:

AMCS, volume 12 (2002)

Temat i słowa kluczowe:

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

Abstract:

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 [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. ; 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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2002

Typ zasobu:

artykuł

Strony:

437-447

Źródło:

AMCS, volume 12, number 3 (2002) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego