Quantifying structural time varying changes in helical data

Spiral structures are one of the most difficult patterns to classify. Spiral rime series data has a helical movement with rime that is both difficult to predict as,well as classify. This paper focusses on ho structural formation about spirals can be useful in providing critical information to a neural network for their recognition. Results are presented on neutral network solutions to the classical two-spiral problem by extracting structural and rotational information from the spiral training data. The results show that in both two and three dimensions, the spirals can be easily recognised by neural networks if they are trained on the temporal structural changes.