@article{id:Siminski2017Robust,
year = {2017},
author = {Krzysztof Siminski},
journal = {Neurocomputing},
pages = {33-43},
volume = {238},
title = {Robust subspace neuro-fuzzy system with data ordering},
doi = {10.1016/j.neucom.2017.01.034},
abstract = {Neuro-fuzzy systems are known for their ability to both
approximate and generalise presented data. In real life data sets 
not always all attributes (dimensions) of data are relevant or have 
the same importance. Some of them may be noninformative or unnecessary. 
This is why subspace technique is applied. Unfortunately this 
technique is vulnerable to noise and outliers that are often present
in real life data. The paper describes a subspace neuro-fuzzy system
with data ordering technique. Data items are ordered and assigned 
with typicalities. Data items with low typicalities have lower 
influence on the elaborated fuzzy model. This technique makes fuzzy
models more robust to noise and outliers. The paper is accompanied 
by numerical experiments on real life data sets.},
}