A noise suppressing distance measure for competitive learning neural networks

Abstract

A measure that equips competitive learning neural networks with noise suppressing capabilities in the learning phase is presented. Analysis shows that weight vectors of neural networks employing the measure are effectively protected from being trained by much shorter (and noisy) input vectors. An ART2a-like scheme is equipped with the measure, while omitting the typical noise-reduction and contrast-enhancement mechanisms of ART2a. Experiments show that this scheme is more robust to noise in the learning phase than ART2a.