Robust subspace neuro-fuzzy system with data ordering

Neuro-fuzzy systems are known for their ability to both approximate and generalize 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.