This paper (Part 1) describes the principles of a novel unsupervised adaptive neural network anomaly detection technique, called componential coding, in the context of condition monitoring of electrical machines. Numerical examples are given to illustrate the technique’ s capabilities. The companion paper (Part 2), which follows, assesses componential coding in its application to real data reco...

E ective Static Debugging via Componential Set Based Analysis

We describe a new measure of Granger causality, componential Granger causality, and show how it can be applied to the identification of the directionality of influences between brain areas with functional neuroimaging data. Componential Granger causality measures the effect of y on x, but allows interaction effects between y and x to be measured. In addition, the terms in componential Granger c...

It is demonstrated that a componential code emerges when a self-organising neural network is exposed to continuous speech. The code’s components correspond to substructures that occur relatively independently of one another: words and phones. A capability for generalisation and discrimination develops without having been optimised explicitly. The componential structure is revealed by optimising...

Recent studies have indicated that people have a strong tendency to compare fractions based on constituent numerators or denominators. This is called componential processing. This study explored whether componential processing was preferred in tasks involving high stimuli variability and high contextual interference, when fractions could be compared based either on the holistic values of fracti...