Quantifying Structural Time Varying Changes in Helical Data

Spiral structures are one of the most difficult patterns to classify. Spiral time series data has a helical movement with time that is both difficult to predict as well as classify. This paper focusses on how structural information about spirals can be useful in providing critical information to a neural network for their recognition. Results are presented on neural 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.