# Improving the generalization ability of neuro-fuzzy systems by ε-insensitive learning

International Journal of Applied Mathematics and Computer Science (2002)

- Volume: 12, Issue: 3, page 437-447
- ISSN: 1641-876X

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topŁęski, Jacek. "Improving the generalization ability of neuro-fuzzy systems by ε-insensitive learning." International Journal of Applied Mathematics and Computer Science 12.3 (2002): 437-447. <http://eudml.org/doc/207600>.

@article{Łęski2002,

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 ε-insensitive learning, where, in order to fit the fuzzy model to real data, the ε-insensitive loss function is used. ε-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 ε-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 ε-insensitive learning are proposed. Finally, examples are given to demonstrate the validity of the introduced methods.},

author = {Łęski, Jacek},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {neural networks; robust methods; fuzzy systems; tolerant learning; generalization control},

language = {eng},

number = {3},

pages = {437-447},

title = {Improving the generalization ability of neuro-fuzzy systems by ε-insensitive learning},

url = {http://eudml.org/doc/207600},

volume = {12},

year = {2002},

}

TY - JOUR

AU - Łęski, Jacek

TI - Improving the generalization ability of neuro-fuzzy systems by ε-insensitive learning

JO - International Journal of Applied Mathematics and Computer Science

PY - 2002

VL - 12

IS - 3

SP - 437

EP - 447

AB - 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 ε-insensitive learning, where, in order to fit the fuzzy model to real data, the ε-insensitive loss function is used. ε-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 ε-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 ε-insensitive learning are proposed. Finally, examples are given to demonstrate the validity of the introduced methods.

LA - eng

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

UR - http://eudml.org/doc/207600

ER -

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