# A fuzzy system with ε-insensitive learning of premises and consequences of if-then rules

International Journal of Applied Mathematics and Computer Science (2005)

- Volume: 15, Issue: 2, page 257-273
- ISSN: 1641-876X

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topŁęski, Jacek, and Czogała, Tomasz. "A fuzzy system with ε-insensitive learning of premises and consequences of if-then rules." International Journal of Applied Mathematics and Computer Science 15.2 (2005): 257-273. <http://eudml.org/doc/207741>.

@article{Łęski2005,

abstract = {First, a fuzzy system based on ifFirst, a fuzzy system based on if-then rules and with parametric consequences is recalled. Then, it is shown that the globalthen rules and with parametric consequences is recalled. Then, it is shown that the global and local ε-insensitive learning of the above fuzzy system may be presented as a combination of both an ε-insensitive gradient method and solving a system of linear inequalities. Examples are given of using the introduced method to design fuzzy models of real-life data. Simulation results show an improvement in the generalization ability of a fuzzy system trained by the new method compared with the traditional and other ε-insensitive learning methods.},

author = {Łęski, Jacek, Czogała, Tomasz},

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

keywords = {global and local ε-insensitive learning; extraction of fuzzy if-then rules; generalization ability; fuzzy system; global and local -insensitive learning},

language = {eng},

number = {2},

pages = {257-273},

title = {A fuzzy system with ε-insensitive learning of premises and consequences of if-then rules},

url = {http://eudml.org/doc/207741},

volume = {15},

year = {2005},

}

TY - JOUR

AU - Łęski, Jacek

AU - Czogała, Tomasz

TI - A fuzzy system with ε-insensitive learning of premises and consequences of if-then rules

JO - International Journal of Applied Mathematics and Computer Science

PY - 2005

VL - 15

IS - 2

SP - 257

EP - 273

AB - First, a fuzzy system based on ifFirst, a fuzzy system based on if-then rules and with parametric consequences is recalled. Then, it is shown that the globalthen rules and with parametric consequences is recalled. Then, it is shown that the global and local ε-insensitive learning of the above fuzzy system may be presented as a combination of both an ε-insensitive gradient method and solving a system of linear inequalities. Examples are given of using the introduced method to design fuzzy models of real-life data. Simulation results show an improvement in the generalization ability of a fuzzy system trained by the new method compared with the traditional and other ε-insensitive learning methods.

LA - eng

KW - global and local ε-insensitive learning; extraction of fuzzy if-then rules; generalization ability; fuzzy system; global and local -insensitive learning

UR - http://eudml.org/doc/207741

ER -

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